Methods, devices, and systems for physiological parameter analysis

ABSTRACT

A method of calculating at least one physiological parameter using a reticulocyte production index (RPI) value can include: measuring a plurality of first glucose levels over a first time period; measuring a first glycated hemoglobin (HbA1c) level corresponding to an end of the first time period; measuring the RPI value; calculating a red blood cell elimination constant (k age ) based on the RPI value; and calculating the at least one physiological parameter selected from the group consisting of: a red blood cell glycation rate constant (k gly ), a red blood cell generation rate constant (k gen ), and an apparent glycation constant (K), based on (1) the plurality of first glucose levels, (2) the first HbA1c level, and (3) the k age . Further, one or more related analyses (e.g., personalized-target glucose range, personalized-target average glucose, cHbA1c, and the like) can be estimated and/or adjusted based on the at least one physiological parameter.

BACKGROUND

The measurement of various analytes within an individual can sometimesbe vital for monitoring the condition of their health. During normalcirculation of red blood cells in a mammal such as a human, glucosemolecules attach to hemoglobin, which is referred to as glycosylatedhemoglobin (also referred to as glycated hemoglobin). The higher theamount of glucose in the blood, the higher the percentage of circulatinghemoglobin molecules with glucose molecules attached. The level ofglycosylated hemoglobin is increased in the red blood cells of subjectswith poorly controlled diabetes mellitus. Since glucose molecules stayattached to hemoglobin for the life of the red blood cells (normally nomore than about 120 days), the level of glycosylated hemoglobin reflectsan average blood glucose level over that period.

Most of hemoglobin is a type called HbA. When glucose molecules attachto HbA molecules, glycosylated HbA is formed, which is referred to asHbA1. HbA1 has three components: HbA1a, HbA1b, and HbA1c. Because aglucose binds more strongly and to a higher degree to HbA1c than HbA1aand HbA1b, a measure of HbA1c in blood (HbA1c test) is often used as anindication of a subject's average blood glucose level over a 120 dayperiod (the average lifetime of a red blood cell). The HbA1c test isperformed by drawing a blood sample from a subject at a medicalprofessional's office, which is then analyzed in a laboratory. The HbA1ctest may be used as a screening and diagnostic test for pre-diabetes anddiabetes. The HbA1c test may be conducted multiple times over a timeperiod to monitor the health of a subject for diagnosis and/or therapydecisions.

Commercially available in vitro blood glucose test strips and in vivosensors (and their related devices and systems) provide glucose levelmeasurements with varying degrees of measurement frequency. Thesedevices can also provide an estimated HbA1c (“eHbA1c”) value. While bothin vitro and in vivo sensors (and their related devices and systems) areknown to be reliable and accurate, when comparisons have been madebetween HbA1c values and eHbA1c values, a notable discrepancy betweenthe two measurements has been observed. Existing eHbA1c methods anddevices, with their reliance on static models, and/or broad assumptionsand/or less robust data, are generally considered to be less reliablethan HbA1c test results. However, HbA1c determination is inconvenientand uncomfortable for subjects, who must periodically have blood drawnfor HbA1c tests and then wait for the results. Additionally, subjectsand healthcare providers would benefit from a more accurate eHbA1c thatwould allow both subjects and their health care providers to monitor andrespond to any changes in eHbA1c. Thus, a need exists for improvedeHbA1c methods and devices.

BRIEF DESCRIPTION OF THE DRAWINGS

The following figures are included to illustrate certain aspects of thepresent disclosure, and should not be viewed as exclusive embodiments.The subject matter disclosed is capable of considerable modifications,alterations, combinations, and equivalents in form and function, withoutdeparting from the scope of this disclosure.

FIG. 1 illustrates an example time line 100 illustrating collection ofat least one HbA1c value and a plurality of glucose levels for a timeperiod.

FIG. 2 illustrates an example of a physiological parameter analysissystem for providing physiological parameter analysis in accordance withsome of the embodiments of the present disclosure.

FIG. 3 illustrates an example of a physiological parameter analysissystem for providing physiological parameter analysis in accordance withsome of the embodiments of the present disclosure.

FIG. 4 illustrates an example of a calculated HbA1c (cHbA1c) report thatmay be generated as an output by a physiological parameter analysissystem in accordance with some of the embodiments of the presentdisclosure.

FIG. 5A illustrates an example of a method of determining apersonalized-target glucose range in accordance with some of theembodiments of the present disclosure.

FIG. 5B illustrates an example of a personalized-target glucose rangereport that may be generated as an output by a physiological parameteranalysis system in accordance with some of the embodiments of thepresent disclosure.

FIG. 6 illustrates an example of a personalized-target average glucosereport that may be generated as an output by a physiological parameteranalysis system in accordance with some of the embodiments of thepresent disclosure.

FIG. 7 illustrates an example of a glucose pattern insight report thatmay be generated as an output by a physiological parameter analysissystem in accordance with some of the embodiments of the presentdisclosure.

FIG. 8 illustrates an example of an in vivo analyte monitoring system inaccordance with some of the embodiments of the present disclosure.

FIGS. 9A-C illustrate a comparison between the laboratory HbA1c levelsat day 200 (±5 days) relative to the estimated HbA1c (eHbA1c) values fortwo different models (9A and 9B) and calculated HbA1c (cHbA1c) valuesfor the kinetic model of the present disclosure (9C).

FIG. 10 illustrates an example study subject's data with the measuredglucose levels (solid line), laboratory HbA1c readings (open circles),cHbA1c model values (long dashed line), and 14-day eHbA1c model values(dotted line).

FIG. 11 illustrates the relationship between steady glucose andequilibrium HbA1c (1) as determined using the standard conversion ofHbA1c to estimated average glucose (dashed line with error bars) and (2)as measured for the 90 participants (solid lines).

FIG. 12 illustrates the relationship between K (dL/mg) and mean glucoselevel target (mg/dl) for varying HbA1c target values using the kineticmodel of the present disclosure.

DETAILED DESCRIPTION

The present disclosure generally describes methods, devices, and systemsfor determining physiological parameters related to the kinetics of redblood cell glycation, elimination, and generation and reticulocytematuration within the body of a subject. Such physiological parameterscan be used, for example, to calculate a more reliable calculated HbA1cand/or a personalized target glucose range, among other things.

Kinetic Model

Formula 1 illustrates the kinetics of red blood cell glycation,elimination, and generation, where “G” is free glucose, “R” is anon-glycated red blood cell, and “GR” is a glycated red blood cell. Therate at which glycated red blood cells (GR) are formed is referred toherein as a red blood cell glycation rate constant (k_(gly) typicallyhaving units of dL*mg⁻¹*day⁻¹).

Over time, red blood cells including the glycated red blood cells arecontinuously eliminated from a subject's circulatory system and new redblood cells are generated, typically at a rate of approximately 2million cells per second. The rates associated with elimination andgeneration are referred to herein as a red blood cell eliminationconstant (k_(age) typically having units of day⁻¹) and a red blood cellgeneration rate constant (k_(gen) typically having units of M²/day),respectively. Since the amount of red blood cells in the body ismaintained at a stable level most of time, the ratio of k_(age) andk_(gen) should be an individual constant that is the square of red bloodcell concentration.

As described previously, HbA1c is a commonly used analyte indicative ofthe fraction of the glycated hemoglobin found in red blood cells.Therefore, a kinetic model can be used, for example, to derive acalculated HbA1c based on at least the glucose levels measured for asubject. However, the kinetic model can also be applied to HbA1. Forsimplicity, HbA1c is uniformly used herein, but HbA1 could besubstituted except in instances where specific HbA1c values are used(e.g., see Equations 15 and 16). In such instances, specific HbA1 valuescould be used to derive similar equations.

Typically, when kinetically modeling physiological processes,assumptions are made to focus on the factors that affect thephysiological process the most and simplify some of the math.

The present disclosure uses only the following set of assumptions tokinetically model the physiological process illustrated in Formula 1.First, glucose concentration is high enough not to be affected by thered blood cell glycation reaction. Second, there is an absence ofabnormal red blood cells that would affect HbA1c measurement, so thehematocrit is constant for the period of interest. This assumption wasmade to exclude extreme conditions or life events that are not normallypresent and may adversely affect the accuracy of the model. Third, theglycation process has first order dependencies on both red blood celland glucose concentrations. Fourth, newly-generated red blood cells havea negligible amount of glycated hemoglobin, based on previous reportsthat reticulocyte HbA1c is very low and almost undetectable. Fifth, redblood cell production inversely correlates with total cellularconcentration, whereas elimination is a first order process.

With the five assumptions described above for this kinetic model, therate of change in glycated and non-glycated red blood cells can bemodeled by differential Equations 1 and 2.

d[GR]/dt=k _(gly)[G][R]−k _(age)[GR]  Equation 1

(d[R])/dt=k _(gen) /C−k _(age)[R]−k _(gly)[G][R]  Equation 2

C is the whole population of red blood cells, where C=[R]+[GR] (Equation2a). C typically has units of M (mol/L), [R] and [GR] typically haveunits of M, and [G] typically has units of mg/dL.

Assuming a steady state, where the glucose level is constant and theglycated and non-glycated red blood cell concentrations remain stable(d[GR]/dt=(d[R])/dt=0), the following two equations can be derived.Equation 3 defines the apparent glycation constant K (typically withunits of dL/mg) as the ratio of k_(gly) and k_(age), whereas Equation 4establishes the dependency between red blood cell generation andelimination rates.

K=k _(gly) /k _(age)=[GR]/[G][R]  Equation 3

k _(gen) /k _(age) =C ²  Equation 4

For simplicity, k_(age) is used hereafter to describe the methods,devices, and systems of the present disclosure. Unless otherwisespecified, k_(gen) can be substituted for k_(age). To substitute k_(gen)for k_(age), Equation 4 would be rearranged to k_(gen)=k_(age)*C².

HbA1c is the fraction of glycated hemoglobin as shown in Equation 5.

HbA1c=[GR]/C=(C−[R])/C  Equation 5

In a hypothetical state when a person infinitely holds the same glucoselevel, HbA1c in Equation 5 can be defined as “equilibrium HbA1c” (EA)(typically reported as a % (e.g., 6.5%) but used in decimal form (e.g.,0.065) in the calculations). For a given glucose level, EA (Equation 6)can be derived from Equations 2a, 3, and 5.

EA=(k _(gly)[G])/(k _(age) +k _(gly)[G])=[G]/(K ⁻¹+[G])  Equation 6

EA is an estimate of HbA1c based on a constant glucose concentration [G]for a long period. This relationship effectively approximates theaverage glucose and HbA1c for an individual having a stable day-to-dayglucose profile. EA depends on K, the value of which is characteristicto each subject. Equation 6 indicates that the steady glucose is notlinearly correlated with EA. Steady glucose and EA may be approximatedwith a linear function within a specific range of glucose level, but notacross the full typical clinical range of HbA1c. Furthermore, in reallife with continuous fluctuations of glucose levels, there is noreliable linear relationship between laboratory HbA1c and averageglucose for an individual.

Others have concluded this also and produced kinetic models to correlatea measured HbA1c value to average glucose levels. For example, TheAmerican Diabetes Association has an online calculator for convertingHbA1c values to estimated average glucose levels. However, this model isbased on an assumption that k_(age) and k_(gly) do not substantiallyvary between subjects, which is illustrated to be false in Example 1below. Therefore, the model currently adopted by the American DiabetesAssociation considers k_(age) and k_(gly) as constants and not variableby subject.

A more recent model by Higgens et al. (Sci. Transl. Med. 8, 359ra130,2016) has been developed that removed the assumption that red blood celllife is constant. However, the more recent model still assumes thatk_(gly) does not substantially vary between subjects.

In contrast, both k_(age) and k_(gly) are variables for the kineticmodels described herein. Further, a subject's k_(gly) is used in someembodiments to derive personalized parameters relating to the subject'sdiabetic condition and treatment (e.g., a medication dosage, asupplement dosage, an exercise plan, a diet/meal plan, and the like).

Continuing with the kinetic model of the present disclosure, the HbA1cvalue (HbA1c_(t)) at the end of a time period t (Equation 7) can bederived from Equation 1, given a starting HbA1c (HbA1c₀) and assuming aconstant glucose level [G] during the time period.

HbA1c _(t) =EA+(HbA1c ₀ −EA)*e ^(−(k) ^(gly) ^([G]+k) ^(age)^()t)  Equation 7

To accommodate changing glucose levels over time, each individual'sglucose history is approximated as a series of time intervals t_(i) withcorresponding average glucose levels [G_(i)]. Applying Equation 7recursively, HbA1c_(z) at the end of time interval t_(z) can beexpressed by Equation 8 for numerical calculations.

HbA1c _(z) =EA _(z)(1−D _(z))+Σ_(i=1) ^(z-1)[EA _(i)(1−D _(i))Π_(j=i+1)^(z) D _(j)]+HbA1c ₀Π_(j=1) ^(z) D _(j)  Equation 8

where the decay term D _(i) =e ^(−(k) ^(gly) ^([G) ^(i) ^(]+k) ^(age)^()t) ^(i)   (Equation 8a).

When solving for k_(age) and k_(gly) using Equations 6, 7, or 8, k_(age)and k_(gly) may be bounded to reasonable physiological limits, by way ofnonlimiting example, of 5.0*10⁻⁶ dL*mg⁻¹*day⁻¹<k_(gly)<8.0*10⁻⁶dL*mg⁻¹*day⁻¹ and 0.006 day⁻¹<k_(age)<0.024 clay⁻¹. Additionally oralternatively, an empirical approach using theBroyden-Fletcher-Goldfarb-Shanno algorithm can be used with estimatedinitial values for k_(gly) and k_(age) (e.g., k_(gly)=4.4*10⁻⁶dL*mg⁻¹*day⁻¹ and k_(age)=0.0092 day⁻¹). The more glucose level datapoints and measured HbA1c data points, the more accurate thephysiological parameters described herein are.

The value for time interval t_(i) can be selected (e.g., by a user ordeveloper, or by software instructions being executed on one or moreprocessors) based on a number of factors that can vary betweenembodiments and, as such, the value of time interval t_(i) may vary. Onesuch factor is the duration of time from one glucose data value (e.g., ameasured glucose level at a discrete time, a value representative ofglucose level for a particular time period across multiple discretetimes, or otherwise) to another within the individual's glucose history.That duration of time between glucose data values can be referred to astime interval t_(g). Time interval t_(g) can vary across theindividual's glucose history such that a single glucose history can havea number of different values for time interval t_(g). Numerous exampleembodiments leading to different values of time interval t_(g) aredescribed herein. In some embodiments of glucose monitoring systems,glucose data points are determined after a fixed time interval t_(g)(e.g., every minute, every ten minutes, every fifteen minutes, etc.) andthe resulting glucose history is a series of glucose data points witheach point representing the glucose at the expiration of or across thefixed time interval t_(g) (e.g., a series of glucose data points at oneminute intervals, etc.).

In other embodiments, glucose data points are taken or determined atmultiple different fixed time intervals t_(g). For example, in someflash analyte monitoring systems (described in further detail herein), auser may request glucose data from a device (e.g., a sensor controldevice) that stores glucose data within a recent time period (e.g., themost recent fifteen minutes, the most recent hour, etc.) at a firstrelatively shorter time interval t_(g) (e.g., every minute, every twominutes), and all other data (in some cases up to a maximum of eighthours, twelve hours, twenty-four hours, etc.) outside of that recenttime period is stored at a second relatively longer time interval t_(g)(e.g., every ten minutes, every fifteen minutes, every twenty minutes,etc.). The data stored at the second, relatively longer time intervalcan be determined from data originally taken at the relatively shortertime interval t_(g) (e.g., an average, median, or other algorithmicallydetermined value). In such an example the resulting glucose history isdependent on how often a user requests glucose data, and can be acombination of some glucose data points at the first time interval t_(g)and others at the second time interval t_(g). Of course, more complexvariations are also possible with, for example, three or more timeintervals t_(g). In some embodiments, glucose data collected with ad hocadjunctive measurements (e.g., a finger stick and test strip) can alsobe present, which can result in even more variations of time intervalt_(g).

An example analysis performed on glucose histories for a sample ofsubjects (approximately 400) where glucose data points were generallypresent at time intervals t_(g) of one to fifteen minutes, indicatedthat a value for time interval t_(i) within the range of three hours (orabout three hours) to twenty four hours (or about twenty four hours)could be selected without significant loss of accuracy. Generally,shorter time intervals t_(i) resulted in higher accuracy than longerones, and time interval t_(i) values closer to three hours were the mostaccurate. Time interval t_(i) values less than three hours may begin toexhibit loss of accuracy due to numerical rounding errors. Theserounding errors can be reduced by using longer digit strings at theexpense of processing load and computing time. It should be noted thatother values of time interval t_(i) outside of the range of 3 to 24hours may be suitable depending on the desired accuracy levels and otherfactors, such as the average time interval t_(g) between glucose datapoints.

Another factor in selection of time interval t_(i) is the existence ofgaps, or missing data, in the individual's glucose history, where thegaps are longer or significantly longer than the longest time intervalt_(g). The existence of one or more such gaps can potentially lead toresults bias. These gaps can result, for example, from the inability tocollect glucose data across a certain time period (e.g., the user wasnot wearing a sensor, the user forgot to scan the sensor for data, afault occurred, etc.). The presence of gaps and their duration should beconsidered in selecting time interval t_(i). Generally, the number andduration of gaps should be minimized (or eliminated) where possible. Butsince gaps of this type are often difficult to eliminate, to the extentsuch gaps exist, in many embodiments the selection of time intervalt_(i) should be at least twice the duration of the largest (maximum) gapbetween glucose data points. For example, if time interval t_(i) isselected to be 3 hours, then the maximum gap should be no longer than 90minutes, if time interval t_(i) is selected to be 24 hours, then thelargest gap should be no longer than 12 hours, and so forth.

The value HbA1c_(z) is the estimated HbA1c of the present kinetic model,which is referred to herein as cHbA1c (calculated HbA1c) to distinguishfrom other eHbA1c described herein.

As described previously and illustrated in Equation 8, EA_(i) and D_(i)are both affected by glucose level [G_(i)], k_(gly), and k_(age). Inaddition, D_(i) depends on the length of the time interval t_(i).Equation 8 is the recursive form of Equation 7. Equations 7 and 8describe the relationship among HbA1c, glucose level, and individual redblood cell kinetic constants k_(gly) and k_(age).

k_(age) can be directly measured through expensive and laboriousmethods. Herein, the kinetic model is extended to incorporatereticulocyte maturation as a method for estimating k_(age).

Reticulocytes are immature red blood cells and typically account forabout 1% of the total red blood cells. The rate at which reticulocytesmature into mature red blood cells is k_(mat) (typically having units ofday⁻¹). The maturation half-life for a normal reticulocyte is about 4.8hours, which provides for Equation 9.

k _(mat)=ln 2/(4.8 hours)=3.47 day⁻¹  Equation 9

The kinetic model makes two assumptions: (1) all red blood cells arereticulocytes at time 0 and (2) reticulocytes are not eliminated (thatis, reticulocytes mature to mature red blood cells and do not die). Theprobability density of reticulocyte age (p_(RET)) can be represented byEquation 10.

p _(RET)(τ)=(k _(age)/(1−ln 2))*e ^(−k) ^(mat) ^(*τ)  Equation 10

where τ is the cell age.

A reticulocyte production index (RPI), also known as a correctedreticulocyte count (CRC), is the percentage of total red blood cellsthat are reticulocytes. Therefore, RPI is the integral of p_(RET) overcell age as shown in Equation 11, where RPI is the decimal form of thereported RPI (e.g., RPI reported at 2% is 0.02 in Equation 11).

RPI=∫₀ ^(∞) p _(RET)(τ)dτ=k _(age)/(k _(mat)*(1−ln 2))  Equation 11

Assuming the typical k_(mat) is 3.47 day⁻¹, k_(age) can be estimatedfrom a measured RPI. RPI can be determined by normal methods. Forexample, RPI can be determined by measuring a hematocrit percentage(HM_(m)), measuring a percentage of reticulocytes (RP) in an RNA dyedblood smear, determining a maturation correction (MC) from the measuredhematocrit percentage, and calculating the RPI based on Equation 12,where RP and HM_(m) is used as the percentage values not the decimalform (i.e., RP reported at 3% is 3 in the equation not 0.03).

RPI=(RP*HM _(m) /HM _(n))/MC  Equation 12

where HM_(n) is the normal hematocrit value (typically 45).

Unless otherwise specified, the typical units described are associatedwith their respective values. One skilled in the art would recognizeother units and the proper conversions. For example, [G] is typicallymeasured in mg/dL but could be converted to M using the molar mass ofglucose. If [G] is used in M or any other variable is used withdifferent units, the equations herein should be adjusted to account fordifferences in units.

Calculating Physiological Parameters from the Kinetic Model

Embodiments of the present disclosure provide kinetic modeling of redblood cell glycation, elimination, and generation and reticulocytematuration within the body of a subject.

The physiological parameter k_(age) can be estimated from one or moreRPI measurements. While k_(age) can be estimated using Equation 11 abovefrom a single RPI measurement, two or more RPI measurements may increasethe accuracy of the RPI value. Further, RPI can change over time, inresponse to treatment, and in response to the improvement or worseningof a disease state. Therefore, while RPI can be measured be measured inany desired intervals of time (e.g., weekly to annually), preferably RPIis measured once every three to six months.

Once k_(age) is calculated, the physiological parameters k_(gly) and/orK can be estimated from the equations described herein given at leastone measured HbA1c value (also referred to as HbA1c level measurement)and a plurality of glucose levels (also referred to as glucose levelmeasurements) over a time period immediately before the HbA1cmeasurement.

FIG. 1 illustrates an example time line 100 illustrating a collection ofat least one measured HbA1c value 102 a, 102 b, 102 c, a plurality ofglucose levels 104 a and 104 b, and at least one measured RPI value 110a, 110 b, 110 c over time periods 106 and 108.

The number of measured HbA1c values 102 a, 102 b, 102 c needed tocalculate k_(gly) and/or K depends on the frequency and duration of theplurality of glucose levels. The number of measured RPI values 110 a,110 b, 110 c needed to calculate k_(age) depends on the stability ofindividual k_(mat) and its deviation to typical k_(mat) (3.47 day⁻¹).Preferably RPI is measured once every three to six months but can bemeasured monthly or weekly, if needed.

In a first embodiment, one measured RPI value 110 b can be used tocalculate k_(age), and one measured HbA1c 102 b can be used along withthe calculated k_(age) and a plurality of glucose measurements over timeperiod 106 to calculate k_(gly) and/or K. Such embodiments areapplicable to subjects with steady daily glucose measurements for a longtime period 106 (e.g., over about 200 days). K may be calculated at timepoint 101 with Equation 6 by replacing EA with the measured HbA1c value102 b and [G] with daily average glucose over time period 106. k_(gly)may then be calculated from Equation 3. Therefore, in this embodiment,an initial HbA1c level measurement 102 a is not necessarily required.

Because a first HbA1c value is not measured, the time interval 106 ofinitial glucose level measurements with frequent measurements may needto be long to obtain an accurate representation of average glucose andreduce error. Using more than 100 days of steady glucose pattern forthis method may reduce error. Additional length like 200 days or more or300 days or more further reduces error.

Embodiments where one measured HbA1c value 102 b can be used include atime period 106 about 100 days to about 300 days (or longer) withglucose levels being measured at least about 72 times per day (e.g.,about every 20 minutes) to about 96 times per day (e.g., about every 15minutes) or more often. Further, in such embodiments, the time betweenglucose level measurements may be somewhat consistent where an intervalbetween two glucose level measurements should not be more than about anhour. Some missing data glucose measurements are tolerable when usingonly one measured HbA1c value. Increases in missing data may lead tomore error.

Alternatively, in some instances where one measured HbA1c value 102 b isused, the time period 106 may be shortened if a subject has an existingglucose level monitoring history with stable, consistent glucoseprofile. For example, for a subject who has been testing for a prolongedtime (e.g., 6 months or longer) but, perhaps, at less frequent orregimented times, the existing glucose level measurements can be used todetermine and analyze a glucose profile. Then, if more frequent andregimented glucose monitoring is performed over time period 106 (e.g.,about 72 times to about 96 times or more per day over about 14 days ormore) followed by measurement of HbA1c 102 b and RPI 110 b, the foursets of data in combination may be used to calculate one or morephysiological parameters (k_(gly), k_(age), and/or K) at time point 101.

Alternatively, in some embodiments, one or more measured RPI values 110a, 110 b, two measured HbA1c values (a first measured HbA1c value 102 aat the beginning of a time period 106 and a second measured HbA1c value102 b at the end of the time period 106), and a plurality of glucoselevels 104 a measured during the time period 106 may be used tocalculate one or more physiological parameters (k_(gly), k_(age), and/orK) at time point 101. In these embodiments, Equation 11 may be used tocalculate k_(age), and Equation 8 may be used to calculate k_(gly)and/or K at time point 101. In such embodiments, the plurality ofglucose levels 104 a may be measured for about 10 days to about 30 daysor longer with measurements being, on average, about 4 times daily(e.g., about every 6 hours) to about 24 times daily (e.g., about every 1hour) or more often.

In the foregoing embodiments, the RPI value(s) can be measured at a timeother than as illustrated because measured RPI values are relativelystable over time. Therefore, the RPI value(s) can be measured at anytime during time period 106 and be applicable to these embodiments.

The foregoing embodiments are not limited to the example glucose levelmeasurement time period and frequency ranges provided. Glucose levelsmay be measured over a time period of about a few days to about 300 daysor more (e.g., about one week or more, about 10 days or more, about 14days or more, about 30 days or more, about 60 days or more, about 90days or more, about 120 days or more, and so on). In some embodiments,the time period is 7 days or more, preferably one to ten months, andless than one year. The frequency of such glucose levels may be, onaverage, about 14,400 times daily (e.g., a time interval t_(g) of aboutevery 6 seconds) (or more often) to about 3 times daily (e.g., a timeinterval t_(g) of about every 8 hours) (e.g., 1,440 times daily (e.g., atime interval t_(g) of about every minute), about 288 times daily (e.g.,a time interval t_(g) of about every 5 minutes), about 144 times daily(e.g., a time interval t_(g) of about every 10 minutes), about 96 timesdaily (e.g., a time interval t_(g) of about every 15 minutes), about 72times daily (e.g., a time interval t_(g) of about every 20 minutes),about 48 times daily (e.g., a time interval t_(g) of about every 30minutes), about 24 times daily (e.g., a time interval t_(g) of aboutevery 1 hour), about 12 times daily (e.g., a time interval t_(g) ofabout every 2 hours), about 8 times daily (e.g., a time interval t_(g)of about every 3 hours), about 6 times daily (e.g., a time intervalt_(g) of about every 4 hours), about 4 times daily (e.g., a timeinterval t_(g) of about every 6 hours), and so on). In some instances,less frequent monitoring (like once or twice daily) may be used wherethe glucose measurements occur at about the same time (within about 30minutes) daily to have a more direct comparison of day-to-day glucoselevels and reduce error in subsequent analyses.

The foregoing embodiments may further include calculating an error oruncertainty associated with the one or more physiological parameters. Insome embodiments, the error may be used to determine if another HbA1cvalue (not illustrated) should be measured near time point 101, if oneor more glucose levels 104 b should be measured (e.g., near time point101), if the monitoring and analysis should be extended (e.g., to extendthrough time period 108 from time point 101 to time point 103 includingmeasurement of glucose levels 104 b during time period 108 andmeasurement of HbA1c value 102 c at time point 103), and/or if thefrequency of glucose level measurements 104 b in an extended time period108 should be increased relative to the frequency of glucose levelmeasurements 104 a during time period 106. In some embodiments, one ormore of the foregoing actions may be taken when the error associatedwith k_(gly), k_(age), and/or K is at or greater than about 15%,preferably at or greater than about 10%, preferably at or greater thanabout 7%, and preferably at or greater than about 5%. When a subject hasan existing disease condition (e.g., cardiovascular disease), a lowererror may be preferred to have more stringent monitoring and less errorin the analyses described herein.

Alternatively or when the error is acceptable, in some embodiments, oneor more physiological parameters (k_(gly), k_(age), and/or K) at timepoint 101 may be used to determine one or more parameters orcharacteristics for a subject's personalized diabetes management (e.g.,a cHbA1c at the end of time period 108, a personalized-target glucoserange, and/or a treatment or change in treatment for the subject in thenear future), each described in more detail further herein. In someinstances, in addition to the foregoing embodiments, an HbA1c value maybe measured at time point 103 and the one or more physiologicalparameters recalculated and applied to a future time period (notillustrated).

Alternatively or additionally, two values for k_(age) can be estimatedusing Equation 8 and Equation 11. A comparison of these two values canbe used to determine if another HbA1c value (not illustrated) should bemeasured near time point 101, if one or more glucose levels 104 b shouldbe measured (e.g., near time point 101), if the monitoring and analysisshould be extended (e.g., to extend through time period 108 from timepoint 101 to time point 103 including measurement of glucose levels 104b and measurement of HbA1c value 102 c at time point 103), and/or if thefrequency of glucose level measurements 104 b in an extended time period108 should be increased relative to the frequency of glucose levelmeasurements 104 a during time period 106. For example, if the twovalues of k_(age) are more than 10% different (e.g., the low value isnot within 10% of the high value based on the high value), theindividual's k_(mat) may be different than the typical k_(mat) (3.47day⁻¹). If a large difference is observed (e.g., more than 20%difference), the individual's k_(mat) could be determined. If theindividual's k_(mat) is stable over a time period (e.g., three to sixmonths), the determined individual's k_(mat) should be used in place ofthe typical k_(mat) in Equation 11 in the methods, systems, and devicesdescribed herein. Fluctuation in k_(mat) could suggest other healthproblems.

The one or more physiological parameters and/or the one or moreparameters or characteristics for a subject's personalized diabetesmanagement can be measured and/or calculated for two or more times(e.g., time point 101 and time point 103) and compared. For example,k_(gly) at time point 101 and time point 103 may be compared. In anotherexample, cHbA1c at time point 103 and at a future time may be compared.Some embodiments, described further herein, may use such comparisons to(1) monitor progress and/or effectiveness of a subject's personalizeddiabetes management and, optionally, alter the subject's personalizeddiabetes management, (2) identify an abnormal or diseased physiologicalcondition, and/or (3) identify subjects taking supplements and/ormedicines that affect red blood cell production and/or affectmetabolism.

Each of the example methods, devices, and systems described herein canutilize the one or more physiological parameters (k_(gly), k_(age), andK) and perform one or more related analyses (e.g., personalized-targetglucose range, personalized-target average glucose, cHbA1c, and thelike). The one or more physiological parameters (k_(gly), k_(age), andK) and related analyses may be updated periodically (e.g., about every 3months to annually). The frequency of updates may depend on, among otherthings, the subject's glucose level and diabetes history (e.g., how wellthe subject stays within the prescribed thresholds), other medicalconditions, and the like.

Other Factors

In the embodiments described herein that apply the one or morephysiological parameters (k_(gly), k_(age), and/or K), one or more othersubject-specific parameters may be used in addition to the one or morephysiological parameters. Examples of subject-specific parameters mayinclude, but are not limited to, vital information (e.g., heart rate,body temperature, blood pressure, or any other vital information), bodychemistry information (e.g., drug concentration, blood levels, troponinlevel, cholesterol level, or any other body chemistry information), mealdata/information (e.g., carbohydrate amount, sugar amount, or any otherinformation about a meal), activity information (e.g., the occurrenceand/or duration of sleep and/or exercise), an existing medical condition(e.g., cardiovascular disease, heart valve replacement, cancer, andsystemic disorder such as autoimmune disease, hormone disorders, andblood cell disorders), a family history of a medical condition, acurrent treatment, an age, a race, a gender, a geographic location(e.g., where a subject grew up or where a subject currently lives), adiabetes type, a duration of diabetes diagnosis, and the like, and anycombination thereof.

Systems

In some embodiments, determining the one or more physiologicalparameters (k_(gly), k_(age), and/or K) for a subject may be performedusing a physiological parameter analysis system.

FIG. 2 illustrates an example of a physiological parameter analysissystem 211 for providing physiological parameter analysis in accordancewith some of the embodiments of the present disclosure. Thephysiological parameter analysis system 211 includes one or moreprocessors 212 and one or more machine-readable storage media 214. Theone or more machine-readable storage media 214 contains a set ofinstructions for performing a physiological parameter analysis routine,which are executed by the one or more processors 212.

In some embodiments, the instructions include receiving inputs 216(e.g., one or more RPI values, one or more glucose levels, one or moreHbA1c levels, one or more physiological parameters (k_(gly), k_(age),and/or K) previously determined, or more other subject-specificparameters, and/or one or more times associated with any of theforegoing), determining outputs 218 (e.g., one or more physiologicalparameters (k_(gly), k_(age), and/or K), an error associated with theone or more physiological parameters, one or more parameters orcharacteristics for a subject's personalized diabetes management (e.g.,cHbA1c, a personalized-target glucose range, an average-target glucoselevel, a supplement or medication dosage, among other parameters orcharacteristics), a matched group of participants, and the like), andcommunicating the outputs 218. In some embodiments, communication of theinputs 216 may be via a user-interface (which may be part of a display),a data network, a server/cloud, another device, a computer, or anycombination thereof, for example. In some embodiments, communication ofthe outputs 218 may be to a display (which may be part of auser-interface), a data network, a server/cloud, another device, acomputer, or any combination thereof, for example.

A “machine-readable medium”, as the term is used herein, includes anymechanism that can store information in a form accessible by a machine(a machine may be, for example, a computer, network device, cellularphone, personal digital assistant (PDA), manufacturing tool, any devicewith one or more processors, and the like). For example, amachine-accessible medium includes recordable/non-recordable media(e.g., read-only memory (ROM), random access memory (RAM), magnetic diskstorage media, optical storage media, flash memory devices, and thelike).

In some instances, the one or more processors 212 and the one or moremachine-readable storage media 214 may be in a single device (e.g., acomputer, network device, cellular phone, PDA, an analyte monitor, andthe like).

In some embodiments, a physiological parameter analysis system mayinclude other components. FIG. 3 illustrates another example of aphysiological parameter analysis system 311 for providing physiologicalparameter analysis in accordance with some of the embodiments of thepresent disclosure.

The physiological parameter analysis system 311 includes healthmonitoring device 320 with subject interface 320A and analysis module320B. The health monitoring device 320 is, or may be, operativelycoupled to data network 322. Also provided in physiological parameteranalysis system 311 is a glucose monitor 324 (e.g., in vivo and/or invitro (ex vivo) devices or system) and a data processingterminal/personal computer (PC) 326, each operatively coupled to healthmonitoring device 320 and/or data network 322. Further shown in FIG. 3is server/cloud 328 operatively coupled to data network 322 forbi-directional data communication with one or more of health monitoringdevice 320, data processing terminal/PC 326 and glucose monitor 324.Physiological parameter analysis system 311 within the scope of thepresent disclosure can exclude one or more of server/cloud 328, dataprocessing terminal/PC 326 and/or data network 322.

In certain embodiments, analysis module 320B is programmed or configuredto perform physiological parameter analysis and, optionally, otheranalyses (e.g., cHbA1c, personalized target glucose range, and othersdescribed herein). As illustrated, analysis module 320B is a portion ofthe health monitoring device 320 (e.g., executed by a processortherein). However, the analysis module 320B may alternatively beassociated with one or more of server/cloud 328, glucose monitor 324,and/or data processing terminal/PC 326. For example, one or more ofserver/cloud 328, glucose monitor 324, and/or data processingterminal/PC 326 may comprise a machine-readable storage medium (ormedia) with a set of instructions that cause one or more processors toexecute the set of instructions corresponding to the analysis module320B.

While the health monitoring device 320, the data processing terminal/PC326, and the glucose monitor 324 are illustrated as each operativelycoupled to the data network 322 for communication to/from theserver/cloud 328, one or more of the health monitoring device 320, thedata processing terminal/PC 326, and the glucose monitor 324 can beprogrammed or configured to directly communicate with the server/cloud328, bypassing the data network 322. The mode of communication betweenthe health monitoring device 320, the data processing terminal/PC 326,the glucose monitor 324, and the data network 322 includes one or morewireless communication, wired communication, RF communication,BLUETOOTH® communication, WiFi data communication, radio frequencyidentification (RFID) enabled communication, ZIGBEE® communication, orany other suitable data communication protocol, and that optionallysupports data encryption/decryption, data compression, datadecompression and the like.

As described in further detail below, the physiological parameteranalysis can be performed by one or more of the health monitoring device320, data processing terminal/PC 326, glucose monitor 324, andserver/cloud 328, with the resulting analysis output shared in thephysiological parameter analysis system 311.

Additionally, while the glucose monitor 324, the health monitoringdevice 320, and the data processing terminal/PC 326 are illustrated aseach operatively coupled to each other via communication links, they canbe modules within one integrated device (e.g., sensor with a processorand communication interface for transmitting/receiving and processingdata).

Measuring Glucose and HbA1c Levels

The measurement of the plurality of glucose levels through the varioustime periods described herein may be done with in vivo and/or in vitro(ex vivo) methods, devices, or systems for measuring at least oneanalyte, such as glucose, in a bodily fluid such as in blood,interstitial fluid (ISF), subcutaneous fluid, dermal fluid, sweat,tears, saliva, or other biological fluid. In some instances, in vivo andin vitro methods, devices, or systems may be used in combination.

Examples of in vivo methods, devices, or systems measure glucose levelsand optionally other analytes in blood or ISF where at least a portionof a sensor and/or sensor control device is, or can be, positioned in asubject's body (e.g., below a skin surface of a subject). Examples ofdevices include, but are not limited to, continuous analyte monitoringdevices and flash analyte monitoring devices. Specific devices orsystems are described further herein and can be found in U.S. Pat. No.6,175,752 and U.S. Patent Application Publication No. 2011/0213225, theentire disclosures of each of which are incorporated herein by referencefor all purposes.

In vitro methods, devices, or systems (including those that are entirelynon-invasive) include sensors that contact the bodily fluid outside thebody for measuring glucose levels. For example, an in vitro system mayuse a meter device that has a port for receiving an analyte test stripcarrying bodily fluid of the subject, which can be analyzed to determinethe subject's glucose level in the bodily fluid. Additional devices andsystems are described further below.

As described above the frequency and duration of measuring the glucoselevels may vary from, on average, about 3 times daily (e.g., about every8 hours) to about 14,400 times daily (e.g., about every 10 seconds) (ormore often) and from about a few days to over about 300 days,respectively.

Once glucose levels are measured, the glucose levels may be used todetermine the one or more physiological parameters (k_(gly), k_(age),and/or K) and, in some instances, other analyses (e.g., cHbA1c,personalized target glucose range, and others described herein). In someinstances, such analyses may be performed with a physiological parameteranalysis system. For example, referring back to FIG. 3, in someembodiments, the glucose monitor 324 may comprise a glucose sensorcoupled to electronics for (1) processing signals from the glucosesensor and (2) communicating the processed glucose signals to one ormore of health monitoring device 320, server/cloud 328, and dataprocessing terminal/PC 326.

The measurement of one or more HbA1c levels at the various timesdescribed herein may be according to any suitable method. Typically,HbA1c levels are measured in a laboratory using a blood sample from asubject. Examples of laboratory tests include, but are not limited to, achromatography-based assay, an antibody-based immunoassay, and anenzyme-based immunoassay. HbA1c levels may also be measured usingelectrochemical biosensors.

The frequency of HbA1c level measurements may vary from, on average,monthly to annually (or less often if the average glucose level of thesubject is stable).

Referring back to FIG. 3, in some embodiments, HbA1c levels may bemeasured with a laboratory test where the results are input to theserver/cloud 328, the subject interface 320A, and/or a display from thetesting entity, a medical professional, the subject, or other user.Then, the HbA1c levels may be received by the one or more of healthmonitoring device 320, server/cloud 328, and data processing terminal/PC326 for analysis by one or more methods described herein.

Calculated HbA1c (cHbA1c)

After one or more physiological parameters (k_(gly), k_(age), and/or K)are calculated, a plurality of glucose measurements may be taken for afollowing time period and used for calculating HbA1c during and/or atthe end of the following time period. For example, referring back toFIG. 1, one or more physiological parameters (k_(gly), k_(age), and/orK) may be calculated at time point 101 based on one or more measured RPIvalues 110 a, 110 b, measurements of the plurality of glucose levels 104a over time period 106, a measured HbA1c level 102 b at the end of timeperiod 106, and optionally a measured HbA1c level 102 a at the beginningof time period 106. Then, for a subsequent time period 108, a pluralityof glucose levels 104 b may be measured. Then, during and/or at the endof the time period 108, Equation 8 can be used to determine a cHbA1cvalue (HbA1c_(z) of Equation 8) where HbA1c₀ is the measured HbA1c level102 b at the end of time period 106 (which is the beginning of timeperiod 108), [G_(i)] are the glucose levels or averaged glucose levelsduring time period 108 (or the portion of time period 108 where cHbA1cis determined during the time period 108), and the provided one or morephysiological parameters (k_(gly), k_(age), and/or K) corresponding totime point 101 are used.

A subject's cHbA1c may be determined for several successive time periodsbased on the one or more physiological parameters (k_(gly), k_(age),and/or K) determined with the most recently measured HbA1c level, themost recently measured RPI value(s), and the intervening measurements ofglucose levels. The RPI value may be measured periodically (e.g., every6 months to a year) to recalculate k_(age). The most recent RPI value oran average of two or more RPI values can be used in the calculation. TheHbA1c may be measured periodically (e.g., every 6 months to a year) torecalculate the one or more physiological parameters. The time betweenremeasuring the RPI value and the measured HbA1c may depend on (1) theconsistency of the measurements of glucose levels, (2) the frequency ofthe measurements of glucose levels, (3) a subject's and correspondingfamily's diabetic history, (4) the length of time the subject has beendiagnosed with diabetes, (5) changes to a subject's personalizeddiabetes management (e.g., changes in medications/dosages, changes indiet, changes in exercise, and the like), (6) the presence of a diseaseor disorder that effects k_(mat) (e.g., anemia, a bone marrow disease, agenetic condition, an immune system disorder, and combinations thereof).For example, a subject with consistent measurements of glucose levels(e.g., a [G] with less than 5% variation) and frequent measurements ofglucose levels (e.g., continuous glucose monitoring) may measure HbA1clevels less frequently than a subject who recently (e.g., within thelast 6 months) changed the dosage of a glycation medication, even withconsistent and frequent measurements of glucose levels.

FIG. 4, with reference to FIG. 2, illustrates an example of a cHbA1creport that may be generated as an output 218 by a physiologicalparameter analysis system 211 of the present disclosure. The illustratedexample report includes a plot of average glucose level over time. Alsoincluded on the report are the most recently measured RPI value (opencircle), the most recently measured HbA1c level (cross), and cHbA1clevels (asterisks) calculated by the physiological parameter analysissystem 211. While the most recently measured RPI value and the mostrecently measured HbA1c level are illustrated as being measured ondifferent days, the two measurements can be done in the same visit to ahealth care provider.

Two cHbA1c levels are illustrated, but one or more cHbA1c levels may bedisplayed on the report, including a line that continuously trackscHbA1c. Alternatively, the output 218 of the physiological parameteranalysis system 211 may include a single number for a current or mostrecently calculated cHbA1c, a table corresponding to the data of FIG. 4,or any other report that provides a subject, healthcare provider, or thelike with at least one cHbA1c level.

In some instances, the cHbA1c may be compared to a previous cHbA1cand/or a previous measured HbA1c level to monitor the efficacy of asubject's personalized diabetes management. For example, if a dietand/or exercise plan is being implemented as part of a subject'spersonalized diabetes management, with all other factors (e.g.,medication and other diseases) equal, then changes in the cHbA1ccompared to the previous cHbA1c and/or the previous measured HbA1c levelmay indicate if the diet and/or exercise plan is effective, ineffective,or a gradation therebetween.

In some instances, the cHbA1c may be compared to a previous cHbA1cand/or a previous measured HbA1c level to determine if another HbA1cmeasurement should be taken. For example, in the absence of significantglucose profile change, if the cHbA1c changes by 0.5 percentage units ormore (e.g., changes from 7.0% to 6.5% or from 7.5% to 6.8%) as comparedto the previous cHbA1c and/or the previous measured HbA1c level, anothermeasured HbA1c level may be tested.

In some instances, a comparison of the cHbA1c to a previous cHbA1cand/or a previous measured HbA1c level may indicate if an abnormal ordiseased physiological condition is present. For example, if a subjecthas maintained a cHbA1c and/or measured HbA1c level for an extendedperiod of time, then if a change in cHbA1c is identified with no otherobvious causes, the subject may have a new abnormal or diseasedphysiological condition. Indications of what that new abnormal ordiseased physiological condition may be gleaned from the one or morephysiological parameters (k_(gly), k_(age), and/or K). Details ofabnormal or diseased physiological conditions relative to the one ormore physiological parameters are discussed further herein.

Personalized-Target Glucose Range

Typically, the glucose levels in subjects with diabetes are preferablymaintained between 54 mg/dL and 180 mg/dL. However, the kinetic modeldescribed herein (see Equation 6) illustrates that intracellular glucoselevels are dependent on physiological parameters k_(gly), k_(age), andK. Therefore, a measured glucose level may not correspond to the actualphysiological conditions in a subject. For example, a subject with ahigher than normal K may glycate glucose more readily. Therefore, a 180mg/dL measured glucose level may be too high for the subject and, in thelong run, potentially worsen the effects of the subject's diabetes. Inanother example, a subject with a lower than normal k_(gly) may glycateglucose to a lesser degree. Accordingly, at a 54 mg/dL glucose level,the subject's intracellular glucose level may be much lower making thesubject feel weak and, in the long term, lead to the subject beinghypoglycemic.

Using the accepted normal lower glucose limit (LGL) and the acceptednormal HbA1c upper limit (AU), equations for a personalized lowerglucose limit (GL) (Equation 13) and a personalized upper glucose limit(GU) (Equation 14) can be derived from Equation 6.

GL=(LGL*k _(gly) ^(ref))/k _(gly) ^(sub)  Equation 13

where k_(gly) ^(ref) is the k_(gly) for a normal person and k_(gly)^(sub) is the subject's k_(gly).

GU=AU/(K(1−AU))  Equation 14

Equation 13 is based on k_(gly) because the lower limit of a glucoserange is based on an equivalent intracellular glucose level. Equation 14is based on K because the upper limit of a glucose range is based on anequivalent extracellular glucose level (e.g., the accepted normal HbA1cupper limit).

The currently accepted values for the foregoing are LGL=54 mg/dL,k_(gly) ^(ref)=6.2*10⁻⁶ dL*mg⁻¹*day⁻¹, and AU=0.08 (i.e., 8%). Using thecurrently accepted values Equations 15 and 16 can be derived.

GL=3.35*10⁻⁴ day⁻¹ /k _(gly) ^(sub)  Equation 15

GU=0.087/K  Equation 16

FIG. 5A illustrates an example of a method of determining apersonalized-target glucose range 530. A desired intracellular glucoserange 532 (e.g., the currently accepted glucose range) having a lowerlimit 534 and an upper limit 536 can be personalized using one or moredetermined physiological parameters (k_(gly), k_(age), and/or K) 538using Equation 13 and Equation 14, respectively. This results in apersonalized lower glucose limit (GL) 540 (Equation 13±7%) and apersonalized upper glucose limit (GU) 542 (Equation 14±7%) that definethe personalized-target glucose range 530. After one or morephysiological parameters (k_(gly), k_(age), and/or K) are calculated, apersonalized-target glucose range may be determined where the lowerglucose limit may be altered according to Equation 13 (or Equation 15)±7% and/or the upper glucose limit may be altered according to Equation14 (or Equation 16) ±7%. The ±7% relative to each of the foregoingcalculated values allows for a different value that is substantiallyclose to the calculated value to be used, so that the personalizednature of the personalized-target glucose range 530 is maintained.Alternatively, the ±7% can be ±10%, ±5%, or ±3%.

For example, a subject with a K of 4.5*10⁻⁴ dL/mg and a k_(gly) of7.0*10⁻⁶ dL*mg⁻¹*day⁻¹ may have a personalized-target glucose range ofabout 48±3.4 mg/dL to about 193±13.5 mg/dL. Therefore, the subject mayhave a wider range of acceptable glucose levels than the currentlypracticed glucose range.

FIG. 5B, with reference to FIG. 2, illustrates an example of apersonalized-target glucose range report that may be generated as anoutput 218 by a physiological parameter analysis system 211 of thepresent disclosure. The illustrated example report includes a plot ofglucose level over a day relative to the foregoing personalized-targetglucose range (area between the dashed lines). Alternatively, otherreports may include, but are not limited to, an ambulatory glucoseprofile (AGP) plot, a numeric display of the personalized-target glucoserange with the most recent glucose level measurement, and the like, andany combination thereof.

In another example, a subject with a K of 6.5*10⁻⁴ dL/mg and a k_(gly)of 6.0*10⁻⁶ dL*mg⁻¹*day⁻¹ may have a personalized-target glucose rangeof about 56±3.5 mg/dL to about 134±10 mg/dL. With the much-reduced upperglucose level limit, the subject's personalized diabetes management mayinclude more frequent glucose level measurements and/or medications tostay substantially within the personalized-target glucose range.

In yet another example, a subject with a K of 5.0*10⁻⁴ dL/mg and ak_(gly) of 5.0*10⁻⁶ dL*mg⁻¹*day⁻¹ may have a personalized-target glucoserange of about 67±4.5 mg/dL to about 174±12 mg/dL. This subject is moresensitive to lower glucose levels and may feel weak, hungry, dizzy, etc.more often if the currently practiced glucose range (54 mg/dL and 180mg/dL) were used.

While the foregoing examples all include a personalized glucose lowerlimit and a personalized glucose upper limit, a personalized-targetglucose range may alternatively include only the personalized glucoselower limit or the personalized glucose upper limit and use thecurrently practiced glucose lower or upper limit as the other value inthe personalized-target glucose range.

The personalized-target glucose range may be determined and/orimplemented in a physiological parameter analysis system. For example, aset of instructions or program associated with a glucose monitor and/orhealth monitoring device that determines a therapy (e.g., an insulindosage) may use a personalized-target glucose range in such analysis. Insome instances, a display or subject interface with display may displaythe personalized-target glucose range.

The personalized-target glucose range may be updated over time as one ormore physiological parameters are recalculated.

Personalized-Target Average Glucose

In some instances, a subject's personalized diabetes management mayinclude having an HbA1c value target for a future time point. Forexample, referring to FIG. 1, a subject may have a measured RPI value110 b and a measured HbA1c value 102 b for time point 101 and aplurality of glucose level measurements prior thereto over time period106. The subject's personalized diabetes management may include a targetHbA1c value (AT) for time point 103 that would correlate to improvedhealth for the subject. Equation 17 can be used to calculate apersonalized-target average glucose level (GT) for the next time period108 and be based on the target HbA1c value (AT) and the subject's Kcalculated at time point 101.

GT=AT/(K(1−AT))  Equation 17

In some embodiments, a physiological parameter analysis system maydetermine an average glucose level for the subject during time period108 and, in some instances, display the average glucose level and/or thetarget average glucose level. The subject may use the current averageglucose level and the target average glucose level to self-monitor theirprogress over time period 108. In some instances, the current averageglucose level may be transmitted (periodically or regularly) to a healthcare provider using a physiological parameter analysis system formonitoring and/or analysis.

FIG. 6, with reference to FIG. 2, illustrates an example of apersonalized-target average glucose report that may be generated as anoutput 218 by a physiological parameter analysis system 211 of thepresent disclosure. The illustrated example report includes a plot of asubject's average glucose (solid line) over time and thepersonalized-target average glucose (illustrated at 150 mg/dL, dashedline). Alternatively, other reports may include, but are not limited to,a numeric display of the personalized-target average glucose with thesubject's average glucose level over a given time frame (e.g., the last12 hours), and the like, and any combination thereof.

The personalized-target average glucose may be updated over time as oneor more physiological parameters are recalculated.

Personalized Treatment—Subject Triage

Insulin pumps along with continuous glucose monitoring may be used forsubjects that need tight control of their glucose levels. As illustratedabove, the target glucose range is individualized and based on k_(gly)and/or K. Therefore, in some instances, subjects with a narrowerpersonalize-target glucose range may be stronger candidates for insulinpumps with continuous monitoring. Triage of subjects to be strongercandidates for insulin pumps along with continuous glucose monitoringmay be based on a spread of the personalized-target glucose range,k_(gly), and/or K.

The spread between currently practiced glucose lower or upper limit isabout 126 mg/dL. However, as illustrated above, depending on k_(gly) andK that could narrow to about 78 mg/dL. Some embodiments may involvetriaging a subject to an insulin pump with continuous glucose monitoringwhen the personalized-target glucose range span is about 110 mg/dL orless, preferably about 100 mg/dL or less.

Some embodiments may involve triaging a subject to an insulin pump withcontinuous glucose monitoring when k_(gly) is 6.4*10⁻⁶ dL*mg⁻¹*day⁻¹ orless, when k_(gly) is 6.0*10⁻⁶ dL*mg⁻¹*day⁻¹ or less, when k_(gly) is5.5*10⁻⁶ dL*mg⁻¹*day⁻¹ or less, or when k_(gly) is 5.0*10⁻⁶dL*mg⁻¹*day⁻¹.

Some embodiments may involve triaging a subject to an insulin pump withcontinuous glucose monitoring when K is 5.0*10⁻⁴ dL/mg or greater, whenK is 5.5*10⁻⁴ dL/mg or greater, when K is 5.75*10⁻⁴ dL/mg or greater, orwhen K is 6.0*10⁻⁴ dL/mg or greater.

In some embodiments, triaging a subject to an insulin pump withcontinuous glucose monitoring may be a stepped triage where first asubject's glucose levels are monitored continuously for a reasonabletime period (e.g., about 5 days, about 10 days, about 15 days, about 30days, or more). This continuous monitoring time period can be used toassess if the subject is capable of managing glucose levels effectivelyor if an insulin pump is better, or required.

Whether the triaging is straight to an insulin pump with continuousglucose monitoring or a stepped triage with monitoring before treatmentwith the insulin pump may be determined by the level of the indicators(i.e., the personalized-target glucose range span, k_(gly), K, or anycombination thereof). For example, if k_(gly) is about 6.4*10⁻⁶dL*mg⁻¹*day⁻¹ but the personalized-target glucose range span is about100 mg/dL, the subject may be more suited for a stepped triage ascompared to another subject where the corresponding indicators suggestan insulin pump should be used.

In some embodiments, triage may be based on a lookup table (e.g., storedin a physiological parameter analysis system of the present disclosure).The lookup table may, for example, correlate multiple values to eachother including, but not limited to, one or more physiologicalparameters (k_(gly), k_(age), and/or K), a personalized-target glucoserange span, and/or other factors described herein like an existingmedical condition, a family history of a medical condition, a currenttreatment, an age, a race, a gender, a geographic location, a diabetestype, a duration of diabetes diagnosis, and the like, and anycombination thereof. Columns in the lookup table may, for example,define ranges or limits for the foregoing parameters, and the rows mayindicate a suggested course of action, which may be an output 218 of aphysiological parameter analysis system 211 of FIG. 2. For example, twocolumns may define an upper and lower bound of k_(gly), where each rowcorresponds to a suggested course of action, such as “candidate forinsulin pump,” “candidate for closed-loop control system,” “candidatefor basal/bolus insulin therapy,” “candidate for basal only insulintherapy,” or any such treatment used to control diabetes or affect thesubject's glycation. In some instances, more than one course of actionmay be indicated. Therefore, in this example, a subject triage reportmay simply display the suggested course(s) of action.

Alternatively, the subject triage report may, for example, show a map ofzones corresponding to the course(s) of action on a plot defined by oneor more of the parameters described above relative to the lookup table.Such zones may, in some instances, be defined by the lookup table. Eachzone on the map may be labeled as representing a recommendation and theglycemic parameter point for the subject may be indicated on the map toshow the relevant zone for that subject.

While the two foregoing subject triage reports are examples based onlookup tables, alternatively, the two foregoing subject triage reportscould be based on other correlations (e.g., a mathematical algorithm ormatrix analysis) between (1) one or more physiological parameters(k_(gly), k_(age), and/or K), a personalized-target glucose range span,and/or other factors described herein and (2) a course(s) of action.

As described, a subject's glycation parameters may help healthcareproviders and payors to better determine what therapy tools are mostappropriate for which subjects. For instance, closed-loop insulin pumpsystems are expensive to employ and maintain, but subjects who have ahigh glycation rate may have a very narrow personalized-target glucoserange where the safest treatment is keeping their glucose levels withinsuch ranges using a closed-loop insulin pump system.

In some embodiments, the insulin pumps along with continuous glucosemonitoring may be closed-loop systems. In some embodiments, the insulinpumps along with continuous glucose monitoring may be hybrid-loopsystems. For example, referring back to FIG. 3, a physiologicalparameter analysis system may further include one of the foregoinginsulin pumps in communication with one or more of the components in thephysiological parameter analysis system 311, for example, the glucosemonitor 324 (e.g., a continuous glucose monitoring system) and healthmonitoring device 320.

Personalized Treatment—Titration of Diabetes Medication

In some embodiments, one or more physiological parameters (k_(gly),k_(age), and K) may be used in titrating dosages of diabetes medication(e.g., insulin) given to a subject. For example, referring to FIG. 2, aphysiological parameter analysis system 211 of the present disclosuremay determine or have input (1) one or more physiological parameters,(2) a personalized-target glucose range, and/or (3) apersonalized-target average glucose. Then, when a subsequent glucoselevel is measured the physiological parameter analysis system 211 mayoutput a recommended diabetes medication dosage. An alternative orcomplimentary output 218 may be a glucose pattern insight report.

Examples of glucose pattern insight reports can be found in U.S. PatentApplication Publication Nos. 2014/0188400 and 2014/0350369, the entiredisclosures of each of which are incorporated herein by reference forall purposes. The disclosed analyses and reports in the forgoingapplications may be modified based on the one or more physiologicalparameters (k_(gly), k_(age), and K) of the present disclosure.

For example, FIG. 7, with reference to FIG. 2, illustrates an example ofa glucose pattern insight report that may be an output 218 of aphysiological parameter analysis system 211 (e.g., an insulin titrationsystem). The illustrated glucose pattern insights report incorporates anAGP along with a table of glycemic control measures (or “trafficlights”). As illustrated, the report includes an AGP plot over ananalysis time period (e.g., about one to about four months) thatillustrates the personalized-target average glucose at 120 mg/dL, theaverage glucose levels for the subject over the analysis time period,the 25^(th) to 75^(th) percentile of glucose levels for the subject overthe analysis time period, and the 10^(th) to 90^(th) percentile ofglucose levels for the subject over the analysis time period. Theglucose pattern insight report may further or alternatively display thepersonalized-target glucose range. Additionally, the glucose patterninsight report may optionally further include one or more of: a measuredHbA1c level, a cHbA1c level, the date range over which the averageglucose and related percentiles were determined, and the like.

Below the AGP plot on the glucose pattern insight report is the tablethat correlates one or more (illustrated as three) glycemic controlmeasures to a subject's average glucose levels for a given shortenedtime period of the day over the analysis time period. The correlationdisplays, in this example, as traffic lights (e.g., green (good), yellow(moderate), or high (red)) that correspond to the risk of a conditionbased on the glycemic control measures. Examples of glycemic controlmeasures include, but are not limited to, likelihood of low glucose,likelihood of high glucose, the proximity of the average glucose to thepersonalized-target average glucose, the adherence of the glucose levelsto the personalized-target glucose range, the degree of variability ofthe average glucose below (or above) the personalized-target averageglucose, the degree of variability of the glucose levels outside (belowand/or above) the personalized-target glucose range, and the like.

In some embodiments, the glucose pattern insights report may be used aspart of a diabetes medication titration system, where the traffic lights(or values associated therewith) can drive logic to provide treatmentmodifications such as changing basal dosages of the diabetes medicationor bolus amounts of the diabetes medication associated with meals. Forexample, when used in conjunction with an automatic or semi-automaticsystem for titration, the logic driven by these traffic lights mayprovide recommendations to subjects on dosage adjustments.

The glucose pattern insights report and related analyses thatincorporate the use of the kinetic model described herein may providebetter treatment to subjects with diabetes. For this example, asdescribed above, a subject with a K of 5.0*10⁻⁴ dL/mg and a k_(gly) of5.0*10⁻⁶ dL*mg⁻¹*day⁻¹ may have a personalized-target glucose range ofabout 67±4.5 mg/dL to about 174±12 mg/dL. This subject is more sensitiveto lower glucose levels and may feel weak, hungry, dizzy, etc. moreoften if the currently practiced glucose range (54 mg/dL and 180 mg/dL)were used. The analytical logic used for the glucose pattern insightsreport described herein that uses one or more physiological parameters(k_(gly), k_(age), and K) may include settings that define the risk ofhypoglycemia as traffic lights for “likelihood of low glucose.” Forexample, if the likelihood of low glucose indicates low risk (e.g., agreen traffic light), then it is considered safe to increase insulin. Ifthe likelihood of low glucose indicates moderate risk (e.g., yellowtraffic light), then it is considered that the current risk isacceptable but no further increase of insulin should be made. Finally,if the likelihood of low glucose indicates high risk, then it isrecommended that insulin should be reduced to get the glucose back totolerable levels. For a subject with high risk of hypoglycemia becauseof an increased lower glucose level threshold, the threshold glucosevalues at which moderate and high risk are indicated (e.g., how farbelow the lower glucose level threshold) may be higher than for asubject with a normal lower glucose level threshold.

While the foregoing example discusses a glucose pattern insights reportas the output 218, other outputs using the same logic and analyses maybe used in other embodiments. For example, the output 218 may be valuesof dosage recommendations.

The one or more physiological parameters (k_(gly), k_(age), and K) andrelated analyses (e.g., personalized-target glucose range,personalized-target average glucose, cHbA1c, and the like) may beupdated periodically (e.g., about every 3 months to annually). Thefrequency of updates may depend on, among other things, the subject'sglucose level and diabetes history (e.g., how well the subject stayswithin the prescribed thresholds), other medical conditions, and thelike.

An insulin titration system may also utilize error associated with theone or more physiological parameters (k_(gly), k_(age), and K). Errorvalues can be determined using standard statistical techniques by thoseskilled in the art and may be used as another set of parameters forconfiguring the titration system. For example, the titration system mayuse the reduced amount of acceptable risk for hypoglycemia (i.e., asmaller tolerance to be below the lower glucose level threshold forindicating moderate and high risk) when the lower glucose level of thepersonalized-target glucose range is about 64 mg/dL with an error ofabout 7% or less.

The dosage of diabetes medication (e.g., via titration) may be updatedover time as one or more physiological parameters are recalculated.

Closed-Loop and Hybrid Closed-Loop Control Systems

Closed-loop systems and hybrid closed-loop systems that recommend oradminister insulin dosages to a subject have been developed for insulindelivery based on near real-time glucose readings. These systems areoften based on models describing the subject's physiology, glucosesensor dynamics, and glucose sensor error characteristics. In someembodiments, the one or more physiological parameters (k_(gly), k_(age),and K) and related analyses (e.g., personalized-target glucose range,personalized-target average glucose, cHbA1c, and the like) may beincorporated into the closed-loop system, similarly to what wasdescribed above for insulin titration, in order to better meet the needsof the subject.

Closed-loop systems often are configured to “drive” the subject'sglucose levels inside a target range and/or toward a single glucosetarget, which may be the personalized-target glucose range and/or thepersonalized-target average glucose described herein. For example, forsubjects with high k_(gly) and an increased lower glucose limit fortheir personalized-target glucose range, the controller may drive theirglucose levels in a way to stay above the lower glucose limit based onk_(gly), which avoids lower glucose levels that adversely affect themmore than subjects with a normal glucose range. Similarly, subjects withreduced upper glucose limits for their personalized-target glucose rangemay have the controller of a closed-loop insulin delivery system andhybrid closed-loop insulin delivery system drive glucose to stay belowthe personalized-upper glucose limit to mitigate hyperglycemic effects.

The metrics by which a closed-loop insulin delivery system and hybridclosed-loop insulin delivery system determine a dosage of insulin may beupdated over time as one or more physiological parameters arerecalculated. For example, the personalized-target glucose range and/orpersonalized-target average glucose may be updated when one or morephysiological parameters are recalculated.

Personalized Treatment—Glycation Medication

Diabetes is a disease caused by a subject's pancreas being unable toproduce sufficient (or any) insulin. However, in some instances, asubject's glycation process may be the source of the body not properlycontrolling intracellular glucose. Such subjects may be more responsiveto treatments that use glycation medications rather than traditionaldiabetes treatments. The kinetic model of the present disclosure derivesk_(gly) and/or K (which is based in part on k_(gly)). Therefore, one orboth of these physiological parameters may be used in identifying,treating, and/or monitoring a subject with a glycation disorder.

Some embodiments may involve monitoring k_(gly) and/or K for a subjecton glycation medication and, in some instances, changing a glycationmedication dosage based on changes to k_(gly) and/or K. For example,referring to FIG. 1, some embodiments may involve determining k_(gly1)and/or K₁ at a time point 101 and a corresponding k_(gly2) and/or K₂ attime point 103 (as described above) and treating a subject withglycation medication over time period 108. Then, based on a comparisonof k_(gly1) and/or K₁ to the corresponding k_(gly2) and/or K₂, a dosageand/or type of glycation medication may be altered for a subsequent timeperiod. Then, in some instances, a corresponding k_(gly3) and/or K₃ maybe determined at the end of the subsequent time period for comparison toone or more of the previously determined physiological parameters. Thetime between time point 101 and time point 103 and between time point103 and the time point corresponding to k_(gly3) and/or K₃ should be atleast the expected time for the glycation medication to make ameasurable change in the parameter being monitored, which may depend onthe medication and the dosage.

In some embodiments, an output 218 of the physiological parameteranalysis system 211 of FIG. 2 may be a glycation medication report thatincludes glycation medication and/or dosage recommendations based onk_(gly) and/or K calculated by the physiological parameter analysissystem 211. This output 218 may be displayed for a subject, healthcareprovider, and/or the like to review and adjust the glycation medicationand/or dosage.

Alternatively, the dosage recommendations provide a subject and/orautomated medication delivery system with the next dosage to beadministered. Here, the system guides titration of the medication, wherethe subject may start with the lowest dosage or a recommended initialdosage. The initial dosage may be defined by the current condition ofthe subject, the subject's k_(gly1) and/or K₁, and other factorsdescribed herein. After an appropriate amount of time has passed for theeffects of the current medication dosage to be adequately determined,k_(gly2) and/or K₂ can be determined based on a new measured HbA1c leveland the glucose levels measured during the medication dosage. k_(gly2)and/or K₂ may then be compared to (1) k_(gly1) and/or K₁ and/or (2) atarget k_(gly) and/or a target K to determine if the dosage needs to bechanged. For example, for a high glycator subject taking a medicationthat is intended to lower glycation rate, if k_(gly2) is still higherthan desired, then the dosage recommendation may be increased accordingto (1) standard titration protocols and/or (2) a system that accountsfor how past dosage changes affect the subject (known as controltheory). In another example, if the subject's k_(gly2) is low, then thedosage may be decreased. Medications could also be similarly titrated toaffect K or other parameters. In addition, a similar process could beused to recommend non-medication treatments such as blood transfusion orharvesting by guiding the appropriate amount of blood to be affected.

Using k_(gly) and/or K to monitor glycation medication efficacy andtitration is valuable to healthcare providers for treating subjects withabnormal glycation physiology.

The metrics by which a dosage of glycation medication is determined maybe updated over time as one or more physiological parameters arerecalculated.

Identifying Abnormal or Diseased Physiological Condition

The kinetic modeling, in certain embodiments, provides physiologicalparameters (e.g., k_(gly), k_(age) (or k_(gen)), and/or K) for differenttime periods, where the same parameter is compared between the differenttime periods to indicate abnormal or disease state of the subject.Variation in the k_(gly), k_(age), and/or K in subjects may provide anindication of abnormal or disease condition of the subject. That is,while k_(gly), k_(age), and/or K varies between subjects, a variation ink_(gly), k_(age), and/or K for a single individual are small and slow.Thus, a comparison of k_(gly), k_(age), and/or K at two or moredifferent time periods provides physiological condition information ofthe subject. For example, when a clinically significant change tok_(gly), k_(age), and/or K is observed over time an abnormal or diseasedphysiological condition may, and likely, exists.

For example, when k_(gly) significantly varies over time such that thevariation is clinically significant, such clinically significantvariation can indicate that the glucose transporter level or cellmembrane has changed. Such biological changes may indicate a potentialmetabolic change in the subject's body resulting from the subject'sphysiology under-going a disease state.

When k_(age) and/or k_(gen) varies significantly over time such that thevariation is clinically significant, such clinically significantvariation can indicate changes to the subject's immune system becausethe immune system is designed to recognize cells that need to beremoved.

A clinically significant variation in k_(age) and/or k_(gen) may also oralternatively be associated with the oxygen sensing mechanism in thebody. An increasing k_(age) and/or k_(gen) over time may indicate thatthe subject's body needs the red blood cells to carry more oxygen or theoxygen sensing mechanism is not functioning correctly, either reasonindicating a physiological state change such as for example, blood lossor a disease condition.

In yet another example (in combination or alternative of the foregoingexamples), clinically significant variation in k_(age) and/or k_(gen)may be associated with bone marrow changes. For example, if the bonemarrow suddenly produces a lot more oxygen carrying red blood cells, thesubject's body will respond by killing off or eliminating more red bloodcells. That is, a clinically significant increase in k_(age) and/ork_(gen) may be associated with bone marrow abnormality.

In another example, a hormone disorder can cause a clinicallysignificant variation in k_(age), k_(gen), and K. Hormones can affectheart rate, contraction strength, blood volume, blood pressure, and redblood cell production. Stress hormones such as catecholamines andcortisol stimulate the release of reticulocytes from the bone marrow andpossibly also enhance erythropoiesis. Therefore, large fluctuation onhormone level can change k_(age) and/or k_(gen), and consequently K.

In yet another example, deviations from normal of the k_(gly), k_(age),and/or K may be an indicator of diabetes or pre-diabetes. Using k_(gly),k_(age), and/or K to measure diabetes or pre-diabetes may be moreeffective than standard fasting glucose tests and measured HbA1c. Forinstance, a subject with a measured HbA1c value in the normal range andnormal fasting glucose may have low k_(gly) associated with high glucosevalues at times in the day other than fasting. Therefore, the subjectmay be a candidate for earlier diabetes intervention that otherwise mayhave gone unnoticed based on standard diabetes diagnosis methods.

In another example, for a subject with a newly high measured HbA1c, thestandard diabetes treatments may be employed to lower their HbA1c.However, determining that k_(gly) is abnormal may be an indication thatthe problem is with their glycation physiology rather than theirpancreas, suggesting other more targeted forms of treatment.

Embodiments of the present disclosure include displaying the determinedk_(gly), k_(age), and/or K, the changes in k_(gly), k_(age), and/or Kover time, and/or possible abnormal or diseased physiologicalconditions.

In the manner described herein, in accordance with the embodiments ofthe present disclosure, the physiological parameter analysis provides anindication of a subject's abnormal or disease condition, as well as ananalysis and/or monitoring tool for one or more parameters orcharacteristics for a subject's personalized diabetes management.

Identifying Supplements and/or Medicines

Several supplements and medications interact with the kinetics of redblood cell glycation, elimination, and generation within the body. Forexample, supplements and medicines used by athletes to dope include, butare not limited to, human growth hormones, supplements and medicinesthat increase metabolic levels, and the like. Human growth hormones canincrease red blood cell count and, consequently, increase k_(age). Inanother example, supplements and medicines that increase metaboliclevels (e.g., exercise mimetics like AMPK agonists) can affect k_(gly).Therefore, some embodiments may use one or more physiological parameters(k_(gly), k_(age), and/or K) as an indicator of doping.

In a first example, having one or more physiological parameters(k_(gly), k_(age), and/or K) outside normal ranges may be used, in someinstances, as an indicator of doping.

In another example, once the one or more physiological parameters(k_(gly), k_(age), and/or K) are determined, continuous monitoring overa 10-day or longer period could identify sudden changes in thephysiological parameters that could indicate doping. This could be usedalone or in combination with the foregoing example of the one or morephysiological parameters being outside normal ranges.

Physiological Age

The physiological parameters k_(age) and, consequently, K change due toaging. Therefore, k_(age) and/or K (provided a stable or known change ink_(gly)) may be used as biological markers to calculate a standardizedmetabolic age. Generally, over time, k_(age) decreases and K increases.Using a correlation between k_(age) and/or K and age in healthysubjects, a new subject's metabolic age may be calculated. Thismetabolic age may then be used as an indicator of the new subject's riskfor age-related degenerative conditions like heart disease, Alzheimer's,or osteoporosis. The risk for age-related degenerative conditions may beused in conjunction with family history of age-related degenerativeconditions for proactive screening and/or preventive treatment. Forexample, a 54-year old subject with a metabolic age of 65 with a familyhistory of cardiovascular disease developing later in life may be testedmore often for signs and/or progression of cardiovascular disease than a54-year old subject with a metabolic age of 50 and a similar familyhistory.

Grouping Decisions for Peer-Based Treatments

An individual's health condition may be treated using group programsystems and methods that utilize peer interaction as a motivating force.In some examples, the health condition may include one or more of:pre-diabetes, heart disease, pre-hypertension, hypertension,pre-coronary atherosclerosis, pre-renal failure, combinations thereof,and others. The systems and methods can be used to facilitate thecreation and/or maintenance of a social environment in which theparticipants interact with a facilitator and/or each other to moreeffectively follow a health regimen.

In many embodiments, methods of utilizing such group programs include astep of selecting a group of participants based on a common or similarcharacteristic amongst the participants. This selection can be performedusing one or more of the physiological parameters described herein(e.g., k_(gly), k_(age) (or k_(gen)), K, and/or cHbA1c) that match(e.g., have the same or similar values for the one or more physiologicalparameters). Participant groups can include any number of two or moreparticipants (e.g., 8-16, 12-18, etc.).

One or more body metrics can be used to assess the extent to which atarget or goal for the health regimen has been achieved. One or more ofthese physiological parameters (e.g., k_(gly), k_(age) (or k_(gen)), K,and/or cHbA1c) can be used as the body metric, although such is notrequired. For example, the systems and methods can be used to guideparticipants diagnosed with prediabetes to lose weight to reduce theirrisk of developing diabetes, to guide participants diagnosed withobesity to lose weight through an exercise and/or diet regimen, and forother purposes. While reduction in body weight is a commonly used metricthat can be measured to assess progress towards a goal, other bodymetrics can be used, including but not limited to: body mass index(BMI), body fat percentage, blood pressure, and cholesterol.

In addition to selection of the group, the methods can further includereceiving a body metric measurement over a network from the participantsof the matched group; determining a body metric measurement trend ofeach participant; and providing feedback to the participant based on thebody metric measurement trend of the participant relative to the bodymetric measurement trend of the remainder of the matched group. Thisfeedback can act as a motivator to stimulate further progress towardsthe target or goal. A facilitator leading the matched group and/or theparticipants in the matched group may provide feedback and supporttailored to the matched group overall and/or to individual participantsin the matched group. This process of receiving and analyzingmeasurements and providing feedback can be repeated periodically overthe length of the program for continued motivation and progress.

Grouping a plurality of participants into a matched group functions toestablish a community among participants. In addition to having at leastone physiological parameter in common (e.g., k_(gly), k_(age) (ork_(gen)), K, and/or cHbA1c), grouping decisions can be based on otherfactors, including but not limited to: a characteristic of a common goal(e.g., the goal of losing or gaining a certain percentage (e.g., 5%) ofan individual respective starting weight, the goal of maintainingcurrent starting weight or to attain a particular goal weight, the goalof losing, gaining, maintaining, or attaining a particular level oramount of BMI, body fat percentage, or other body metric measurement,the goal of reducing body fat percentage, a common goal related to ahealth condition, such as preventing development of prediabetes todiabetes, and others); a common medical history (e.g., diagnosis of aparticular condition at approximately the same time (e.g. diagnosed withpre-diabetes within two months of one another, or another suitablethreshold), similar initial body weights, similar initial degree (classor stage) of congestive heart failure or other diagnosis of acardiovascular disease, a similar degree of obesity, a similar stage ofosteoarthritis or other joint disease that affects mobility, a similardiagnosis of depression or obsessive-compulsive disorder, or others);shared personality traits, or similar positions within a personalityspectrum (e.g., similar results of a personality test or otherassessment); a shared lifestyle characteristic or common interest (e.g.,similar dietary restrictions or preferences (e.g., vegetarianism,veganism, nut-free, gluten-free), marriage status (e.g., married,divorced, widowed, single), children status (e.g., existence, age,gender, number of children), pet status (e.g., existence, age, species,number of pets), religious identification, similar hobbies or otherinterests (e.g., sports, television shows, cooking), or others); and/orsimilar personal information (e.g., gender, ethnicity or nationality,age, current geographical area, or occupational field, hometowns,schools attended, employers, or others). The making of groupingdecisions based on having at least one physiological parameter in common(e.g., k_(gly), k_(age) (or k_(gen)), K, and/or cHbA1c) and one or moreof the aforementioned other factors can be a tiered or staged processthat effectively places the various characteristics in a hierarchy ofimportance.

Further examples of group programs utilizing peer-based treatment, wherea grouping decision can be based upon at least one of k_(gly), k_(age)(or k_(gen)), K, and/or cHbA1c are described in the following referencesthat are incorporated by reference herein in their entireties for allpurposes: U.S. Patent Publ. 2013/0117040 (“Method and System forSupporting a Health Regimen”); U.S. Patent Publ. 2014/0214442 (“Systemsand Methods for Tracking Participants in a Health Improvement Program”);U.S. Patent Publ. 2014/0214443 (“Systems and Methods for DisplayingMetrics Associated with a Health Improvement Program”); U.S. PatentPubl. 2014/0222454 (“Systems and Methods that Administer a HealthImprovement Program and an Adjunct Medical Treatment”); and U.S. PatentPubl. 2017/0344726 (“Method and System for Supporting a HealthRegimen”).

Analyte Monitors and Monitoring Systems

Generally, embodiments of the present disclosure are used with or assystems, devices, and methods for measuring glucose and, in someinstances, at least one other analyte in a bodily fluid. The embodimentsdescribed herein can be used to monitor and/or process informationregarding glucose and, in some instances, at least one other analyte.Other analytes that may be monitored include, but are not limited to,glucose derivatives, HbA1c, acetylcholine, amylase, bilirubin,cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB),creatine, creatinine, DNA, fructosamine, glutamine, growth hormones,hormones, ketones, ketone bodies, lactate, peroxide, prostate-specificantigen, prothrombin, RNA, thyroid stimulating hormone, and troponin.The concentration of drugs, such as, for example, antibiotics (e.g.,gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs ofabuse, theophylline, and warfarin, may also be monitored. In embodimentsthat monitor glucose and one or more than one other analytes, each ofthe analytes may be monitored at the same or different times.

The analyte monitors and/or analyte monitoring systems (referred toherein collectively as analyte monitoring systems) used with or assystems, devices, and methods for measuring glucose and, in someinstances, one or more analytes in a bodily fluid may be in vivo analytemonitoring systems or in vitro analyte monitoring systems. In someinstances, systems, devices, and methods of the present disclosure mayuse both in vivo analyte monitoring systems and in vitro analytemonitoring systems.

In vivo analyte monitoring systems include analyte monitoring systemswhere at least a portion of an analyte sensor is, or can be, positionedin the body of a subject to obtain information about at least oneanalyte of the body. In vivo analyte monitoring systems can operatewithout the need for user calibration. Examples of in vivo analytemonitoring systems include, but are not limited to, continuous analytemonitoring systems and flash analyte monitoring systems.

Continuous analyte monitoring systems (e.g., continuous glucosemonitoring systems), for example, are in vivo systems that can transmitdata from a sensor control device to a reader device repeatedly orcontinuously without prompting (e.g., automatically according to aschedule).

Flash analyte monitoring systems (or flash glucose monitoring systems orsimply flash systems), for example, are in vivo systems that cantransfer data from a sensor control device in response to a scan orrequest for data by a reader device, such as with a near fieldcommunication (NFC) or radio frequency identification (RFID) protocol.

In vivo analyte monitoring systems can include a sensor that, whilepositioned in vivo, makes contact with the bodily fluid of the subjectand senses one or more analyte levels contained therein. The sensor canbe part of a sensor control device that resides on the body of thesubject and contains the electronics and power supply that enable andcontrol the analyte sensing. The sensor control device, and variationsthereof, can also be referred to as a “sensor control unit,” an “on-bodyelectronics” device or unit, an “on-body” device or unit, or a “sensordata communication” device or unit, to name a few. As used herein, theseterms are not limited to devices with analyte sensors, and encompassdevices that have sensors of other types, whether biometric ornon-biometric. The term “on body” refers to any device that residesdirectly on the body or in close proximity to the body, such as awearable device (e.g., glasses, watch, wristband or bracelet, neckbandor necklace, etc.).

In vivo analyte monitoring systems can also include one or more readerdevices that receive sensed analyte data from the sensor control device.These reader devices can process and/or display the sensed analyte data,in any number of forms, to the subject. These devices, and variationsthereof, can be referred to as “handheld reader devices,” “readerdevices” (or simply, “readers”), “handheld electronics” (or handhelds),“portable data processing” devices or units, “data receivers,”“receiver” devices or units (or simply receivers), “relay” devices orunits, or “remote” devices or units, to name a few. Other devices suchas personal computers have also been utilized with or incorporated intoin vivo and in vitro monitoring systems.

For example, referring to FIG. 3, a sensor or portion thereof of an invivo analyte monitoring system may be the glucose monitor 324, and thereader device may be the health monitoring device 320. In alternativeembodiments, the in vivo analyte monitoring system may be, in whole, theglucose monitor 324 that transmits data to a health monitoring device320, data network 322, data processing terminal/PC 326, and/orserver/cloud 328.

For in vivo analyte monitoring systems, the determination of one or morephysiological parameters (e.g., k_(gly), k_(age) (or k_(gen)), and/or K)and/or other analyses described herein may be performed within the invivo analyte monitoring system, in some instances. Only thephysiological parameters may, for example, be determined within the invivo analyte monitoring system and transmitted to a suitable othercomponent of a physiological parameter analysis system, which mayperform other analyses described herein. In some embodiments, the invivo analyte monitoring system may only produce output signals thatcorrespond to glucose levels that are received by another component of aphysiological parameter analysis system. In such cases, one or more ofthe other component(s) of the physiological parameter analysis systemmay determine one or more physiological parameters (e.g., k_(gly),k_(age) (or k_(gen)), and/or K) and, in some instances, perform one ormore of the other analyses described herein.

FIG. 8 illustrates an example of an in vivo analyte monitoring system860. For embodiments of the present disclosure this example in vivoanalyte monitoring system 860 monitors glucose and, in some instances,one or more other analytes. Other analytes of interest with respect tohuman physiology may include, for example, lactate, oxygen, pH, A1c,ketones, drug levels, and the like. Any of these analytes may exhibittemperature-insensitive permeability through the polymeric membranecompositions disclosed herein. Both single analytes and any combinationof the foregoing analytes may be assayed.

The in vivo analyte monitoring system 860 comprises a sensor controldevice 862 (which may be at least a portion of the glucose monitor 324of FIG. 3) and a reader device 864 (which may be at least a portion ofthe health monitoring device 320 of FIG. 3) that communicate with eachother over a local communication path (or link) 866, which can be wiredor wireless, uni-directional or bi-directional, and/or encrypted ornon-encrypted. Any suitable electronic communication protocol may beused for each of the local communication paths 866 or links. Forexample, in embodiments where path 866 is wireless, a near fieldcommunication (NFC) protocol, RFID protocol, BLUETOOTH® or BLUETOOTH®Low Energy protocol, WiFi protocol, proprietary protocol, or the likecan be used, including those communication protocols in existence as ofthe date of this filing or their later developed variants.

Reader device 864 (e.g., a dedicated reader, a cellular phone or PDArunning an app, or the like) is also capable of wired, wireless, orcombined communication with a computer system 868 (which may be at leasta portion of the data processing terminal/PC 326 of FIG. 3) overcommunication path (or link) 870 and with a network 872 (which may be atleast a portion of the data network 322 and/or the server/cloud 328 ofFIG. 3), such as the internet or the cloud, over communication path (orlink) 874. Communication with network 872 can involve communication withtrusted computer system 876 within network 872, or though network 872 tocomputer system 868 via communication link (or path) 878. Communicationpaths 870, 874, and 878 can be wireless, wired, or both, can beuni-directional or bi-directional, can be encrypted or non-encrypted,and can be part of a telecommunications network, such as a Wi-Finetwork, a local area network (LAN), a wide area network (WAN), theinternet, or other data network. In some cases, communication paths 870and 874 can be the same path. All communications over paths 866, 870,and 874 can be encrypted and sensor control device 862, reader device864, computer system 868, and trusted computer system 876 can each beconfigured to encrypt and decrypt those communications sent andreceived.

Variants of devices 862 and 864, as well as other components of an invivo-based analyte monitoring system that are suitable for use with thesystem, device, and method embodiments set forth herein, are describedin U.S. Patent Application Publication No. 2011/0213225 (hereinafter the'225 Publication), which is incorporated by reference herein in itsentirety for all purposes.

Sensor control device 862 can include a housing 880 containing in vivoanalyte monitoring circuitry and a power source. In this embodiment, thein vivo analyte monitoring circuitry is electrically coupled with ananalyte sensor 882 that extends through an adhesive patch 884 andprojects away from housing 880. Adhesive patch 884 contains an adhesivelayer (not shown) for attachment to a skin surface of the body of thesubject. Other forms of body attachment to the body may be used, inaddition to or instead of adhesive. Suitable adhesives for inclusion inthe adhesive layer will be familiar to one having ordinary skill in theart.

Sensor 882 is adapted to be at least partially inserted into a tissue ofinterest, such as the dermal layer or subcutaneous layer of the skin.Sensor 882 may comprise a sensor tail of sufficient length for insertionto a desired depth in a given tissue. The sensor tail may comprise asensing region that is active for sensing, and may comprise an enzyme, apolymeric membrane, and other components. One or more analyte levels maybe determined using sensor 882 and undergo communication to readerdevice 864. The analyte may be monitored in any biological fluid such asdermal fluid, plasma, blood, lymph, or the like.

Sensor 882 and any accompanying sensor control electronics can beapplied to the body in any desired manner. For example, an introducermay be present transiently to promote introduction of sensor 882 into atissue. In illustrative embodiments, the introducer may comprise aneedle. It is to be recognized that other types of introducers, such assheaths or blades, may be present in alternative embodiments. Morespecifically, the needle or similar introducer may transiently reside inproximity to sensor 882 prior to insertion and then be withdrawnafterward. While present, the needle or other introducer may facilitateinsertion of sensor 882 into a tissue by opening an access pathway forsensor 882 to follow. For example, the needle may facilitate penetrationof the epidermis as an access pathway to the dermis to allowimplantation of sensor 882 to take place, according to one or moreembodiments. After opening the access pathway, the needle or otherintroducer may be withdrawn so that it does not represent a sharpshazard. In illustrative embodiments, the needle may be solid or hollow,beveled or non-beveled, and/or circular or non-circular incross-section. In more particular embodiments, the needle may becomparable in cross-sectional diameter and/or tip design to anacupuncture needle, which may have a cross-sectional diameter of about250 microns. It is to be recognized, however, that suitable needles mayhave a larger or smaller cross-sectional diameter if needed forparticular applications. In alternative embodiments, the needle orsimilar introducers may be absent, provided sensor 882 is sufficientlyrobust to penetrate a tissue and establish communication with a bodilyfluid of interest.

In some embodiments, a tip of the needle may be angled over the terminusof sensor 882, such that the needle penetrates a tissue first and opensan access pathway for sensor 882. In other illustrative embodiments,sensor 882 may reside within a lumen or groove of the needle, with theneedle similarly opening an access pathway for sensor 882. In eithercase, the needle is subsequently withdrawn after facilitating insertion.

Examples of introducers (also known as insertion devices) are describedin U.S. Patent Application Publication Nos. 2008/0009692, 2011/0319729,2015/0018639, 2015/0025345, and 2015/0173661, all which are incorporatedby reference herein in their entireties and for all purposes.

It is to be recognized that analyte monitoring system 860 may compriseadditional features and functionality that are not necessarily describedherein in the interest of brevity. Accordingly, the foregoingdescription of analyte monitoring system 860 should be consideredillustrative and non-limiting in nature.

After collecting raw data from the subject's body, sensor control device862 can apply analog signal conditioning to the data and convert thedata into a digital form of the conditioned raw data. In someembodiments, this conditioned raw digital data can be encoded fortransmission to another device (e.g., reader device 864), which thenalgorithmically processes that digital raw data into a final formrepresentative of the subject's measured biometric (e.g., a form readilymade suitable for display to the subject or readily used in the analysismodule 320B of FIG. 3). This algorithmically processed data can then beformatted or graphically processed for digital display to the subject.In other embodiments, sensor control device 862 can algorithmicallyprocess the digital raw data into the final form that is representativeof the subject's measured biometric (e.g., analyte level) and thenencode and wirelessly communicate that data to reader device 864, whichin turn can format or graphically process the received data for digitaldisplay to the subject. In other embodiments, sensor control device 862can graphically process the final form of the data such that it is readyfor display, and display that data on a display of sensor control device862 or transmit the data to reader device 864. In some embodiments, thefinal form of the biometric data (prior to graphic processing) is usedby the system (e.g., incorporated into a diabetes monitoring regime)without processing for display to the subject. In some embodiments,sensor control device 862 and reader device 864 transmit the digital rawdata to another computer system for algorithmic processing and display.

Reader device 864 can include a display 886 to output information to thesubject (e.g., one or more physiological parameter or an output derivedtherefrom like cHbA1c) and/or to accept an input from the subject and/orhealthcare provider (e.g., a measured RPI value and/or a measured HbA1cvalue) and an optional input component 888 (or more) (e.g., a button,actuator, touch sensitive switch, capacitive switch, pressure sensitiveswitch, jog wheel or the like) to input data, commands, or otherwisecontrol the operation of reader device 864. In certain embodiments,display 886 and input component 888 may be integrated into a singlecomponent, for example, where the display can measure the presence andlocation of a physical contact touch upon the display, such as a touchscreen subject interface (which may be at least a portion of the subjectinterface 320A of FIG. 3). In certain embodiments, input component 888of reader device 864 may include a microphone and reader device 864 mayinclude software configured to analyze audio input received from themicrophone, such that functions and operation of the reader device 864may be controlled by voice commands. In certain embodiments, an outputcomponent of reader device 864 includes a speaker (not shown) foroutputting information as audible signals. Similar voice responsivecomponents such as a speaker, microphone and software routines togenerate, process, and store voice driven signals may be included insensor control device 862.

Reader device 864 can also include one or more data communication ports890 for wired data communication with external devices such as computersystem 868. Example data communication ports 890 include, but are notlimited to, USB ports, mini USB ports, USB Type-C ports, USB micro-Aand/or micro-B ports, RS-232 ports, Ethernet ports, Firewire ports, orother similar data communication ports configured to connect to thecompatible data cables. Reader device 864 may also include an integratedor attachable in vitro glucose meter, including an in vitro test stripport (not shown) to receive an in vitro glucose test strip forperforming in vitro blood glucose measurements.

Reader device 864 can display the measured biometric data wirelesslyreceived from sensor control device 862 and can also be configured tooutput alarms (e.g., a visual alarm on a display, an auditory alarm, ora combination thereof), alert notifications, glucose levels, etc., whichmay be visual, audible, tactile, or any combination thereof. Furtherdetails and other display embodiments can be found in U.S. PatentApplication Publication No. 2011/0193704, for example, which isincorporated herein by reference in its entirety for all purposes.

Reader device 864 can function as a data conduit to transfer themeasured data from sensor control device 862 to computer system 868 ortrusted computer system 876. In certain embodiments, the data receivedfrom sensor control device 862 may be stored (permanently ortemporarily) in one or more memories of reader device 864 prior touploading to computer system 868, trusted computer system 876, ornetwork 872.

Computer system 868 may be a personal computer, a server terminal, alaptop computer, a tablet, or other suitable data processing device.Computer system 868 can be (or include) software for data management andanalysis and communication with the components in analyte monitoringsystem 860. Computer system 868 can be used by the subject, a medicalprofessional, or other user to display and/or analyze the biometric datameasured by sensor control device 862. In some embodiments, sensorcontrol device 862 can communicate the biometric data directly tocomputer system 868 without an intermediary such as reader device 864,or indirectly using an internet connection (also, in some instances,without first sending to reader device 864). Operation and use ofcomputer system 868 is further described in the '225 Publicationincorporated herein. Analyte monitoring system 860 can also beconfigured to operate with a data processing module (not shown), also asdescribed in the incorporated '225 Publication.

Trusted computer system 876 can be within the possession of themanufacturer or distributor of sensor control device 862, eitherphysically or virtually through a secured connection, and can be used toperform authentication of sensor control device 862, for secure storageof the subject's biometric data, and/or as a server that hosts a dataanalytics program (e.g., accessible via a web browser) for performinganalysis on the subject's measured data.

In vivo analyte monitoring systems can be used in conjunction with or asa portion of an integrated diabetes management system. For example, anintegrated diabetes management system may include an in vivo analytemonitoring system and a supplement/medication delivery system, and morespecifically, an in vivo glucose monitoring system and an insulindelivery system (e.g., an insulin pump). Integrated diabetes managementsystems may be closed-loop, open-loop, or a hybrid thereof. Closed-loopsystems are in full control of analyte measurement times andsupplement/medication dosages and times. Open-loop systems allow asubject to be in full control of analyte measurement times andsupplement/medication dosages and times. Hybrid systems can relyprimarily on a closed-loop system methodology but allow a subject tointervene.

In vitro analyte monitoring systems contact a bodily fluid outside ofthe body. In some instances, in vitro analyte monitoring systems includea meter device that has a port for receiving the bodily fluid of thesubject (e.g., on an analyte test strip/swab or via collection of thebodily fluid), which can be analyzed to determine the subject's analytelevel.

EXAMPLE EMBODIMENTS

Examples of embodiments of the present disclosure include Embodiment A,Embodiment B, Embodiment C, Embodiment D, Embodiment E, Embodiment F,Embodiment G, Embodiment H, Embodiment I, and Embodiment J. Combinationsof such embodiments are also included as part of the present disclosure.

Embodiment A is a method comprising: providing (or receiving) aplurality of glucose levels over a first time period; providing (orreceiving) a first glycated hemoglobin (HbA1c) level corresponding to anend of the first time period; providing (or receiving) a reticulocyteproduction index (RPI) value corresponding to a time during the firsttime period; determining (e.g., calculating) a red blood cellelimination constant (k_(age)) from the RPI value; and determining(e.g., calculating) at least one physiological parameter selected fromthe group consisting of: a red blood cell glycation rate constant(k_(gly)), a red blood cell generation rate constant (k_(gen)), and anapparent glycation constant (K), based on (1) the plurality of glucoselevels, (2) the first HbA1c level, and (3) the k_(age).

Embodiment B is a method comprising: measuring a plurality of glucoselevels over a first time period; measuring a first glycated hemoglobin(HbA1c) level corresponding to an end of the first time period;measuring a reticulocyte production index (RPI) value corresponding to atime during the first time period; determining (e.g., calculating) a redblood cell elimination constant (k_(age)) from the RPI value; anddetermining (e.g., calculating) at least one physiological parameterselected from the group consisting of: a red blood cell glycation rateconstant (k_(gly)), a red blood cell generation rate constant (k_(gen)),and an apparent glycation constant (K), based on (1) the plurality ofglucose levels, (2) the first HbA1c level, and (3) the k_(age).

Embodiment A or B may include additional elements, which may include,but are not limited to: Element 1: wherein the first time period isabout 300 days or longer and the plurality of glucose levels occur onaverage about 96 or more times daily; Element 2: the method furthercomprising measuring a second HbA1c level corresponding to a beginningof the first time period and, in some instances, wherein the first timeperiod is about 30 days or longer, and the plurality of glucose levelsoccur on average about 24 or more times daily; Element 3: wherein atleast some of the plurality of glucose levels are measured with an invivo analyte sensor having a portion positioned to be in contact with abodily fluid, the in vivo analyte sensor generating signalscorresponding to a plurality of glucose levels in the bodily fluid;Element 4: Element 3 and wherein the bodily fluid comprises a fluidselected from the group consisting of: blood, dermal fluid, interstitialfluid, or a combination thereof; Element 5: Element 3 and wherein the invivo analyte sensor is a component of a closed-loop control system or ahybrid closed-loop control system for delivering an insulin dosage;Element 6: wherein at least some of the plurality of glucose levels aremeasured by a continuous glucose monitoring system; Element 7: whereinat least some of the plurality of glucose levels are input based on anin vitro glucose level measurement; Element 8: Element 7 and wherein thein vitro glucose level measurement measures the plurality of glucoselevels in a fluid selected from the group consisting of: blood,interstitial fluid, subcutaneous fluid, dermal fluid, sweat, tears,saliva, or a combination thereof; Element 9: the method furthercomprising displaying the at least one physiological parameter; Element10: the method further comprising calculating an error associated withthe at least one physiological parameter; and measuring at least one newglucose level and/or measuring at least one new HbA1c level when theerror is at or greater than about 7%; Element 11: the method furthercomprising calculating a metabolic age based on k_(age) and/or K;Element 12: the method further comprising calculating apersonalized-target glucose range based on the at least onephysiological parameter; Element 13: the method further comprisingcalculating a personalized-target average glucose based on the at leastone physiological parameter; Element 14: the method further comprisingcalculating a cHbA1c based on the at least one physiological parameterand a plurality of glucose levels over a second time period followingthe first time period and, in some instances, wherein the plurality ofglucose levels over the second time period are at a plurality of timesseparated by a time interval t_(i) that is (a) greater than or equal tothree hours and less than or equal to twenty-four hours and/or (b) atleast twice a maximum gap duration between temporally adjacent glucoselevels in the plurality of glucose levels over the second time period;Element 15: the method further comprising triaging a subject's treatmentbased on the at least one physiological parameter; Element 16: themethod further comprising adjusting a dosage of diabetes medicationbased on the at least one physiological parameter; Element 17: themethod further comprising adjusting a dosage of glycation medicationbased on the at least one physiological parameter; Element 18: themethod further comprising determining an abnormal or diseasedphysiological condition of a subject based on the at least onephysiological parameter; and Element 19: the method further comprisingdetermining a type of a medication or supplement in a subject's bodybased on the at least one physiological parameter. Examples ofcombinations of elements include, but are not limited to, two or more ofElements 11-19 in combination; Elements 6 and 7 in combination,optionally in further combination with Element 8; Element 6 incombination with Element 3, optionally in further combination with oneor both of Elements 4-5; Elements 9-10 in combination; Element 9 and/orElement 10 in combination with one or more of Elements 11-19; one ormore of Elements 3-8 in combination with one or more of Elements 11-19;and Element 1 or Element 2 in combination with one or more of Elements3-19 including any of the foregoing combinations of Elements 3-19.

Embodiment C is an apparatus comprising: one or more processors; and amemory operatively coupled to the one or more processors and havinginstructions stored thereon which, when executed by the one or moreprocessors, causes the one or more processors to: receive a plurality ofglucose levels over a first time period; receive a first glycatedhemoglobin (HbA1c) level corresponding to an end of the first timeperiod; receive a reticulocyte production index (RPI) valuecorresponding to a time during the first time period; determine a redblood cell elimination constant (k_(age)) from the RPI value; anddetermine at least one physiological parameter selected from the groupconsisting of: a red blood cell glycation rate constant (k_(gly)), a redblood cell generation rate constant (k_(gen)), and an apparent glycationconstant (K), based on (1) the plurality of glucose levels, (2) thefirst HbA1c level, and (3) the k_(age). Alternatively, the k_(age)calculated based on the RPI can be received instead of the RPI.

Embodiment D is a system comprising: an analyte sensor configured tomeasure a glucose level in a bodily fluid; and a monitoring devicecomprising: one or more processors; and a memory operatively coupled tothe one or more processors and having instructions stored thereon which,when executed by the one or more processors, causes the one or moreprocessors to: receive a plurality of glucose levels over a first timeperiod from the analyte sensor; receive a first glycated hemoglobin(HbA1c) level corresponding to an end of the first time period; receivea reticulocyte production index (RPI) value corresponding to a timeduring the first time period; determine a red blood cell eliminationconstant (k_(age)) from the RPI value; and determine at least onephysiological parameter selected from the group consisting of: a redblood cell glycation rate constant (k_(gly)), a red blood cellgeneration rate constant (k_(gen)), and an apparent glycation constant(K), based on (1) the plurality of glucose levels, (2) the first HbA1clevel, and (3) the k_(age). Alternatively, the k_(age) calculated basedon the RPI can be received instead of the RPI.

Embodiment E is a system comprising: one or more processors; an in vivoanalyte sensor having a portion positioned to be in contact with abodily fluid, the in vivo analyte sensor generating signalscorresponding to glucose levels in the bodily fluid; a transmittercoupled to the in vivo analyte sensor and to at least one processor ofthe one or more processors; and a memory operatively coupled to the oneor more processors and having instructions stored thereon which, whenexecuted by the one or more processors, causes the one or moreprocessors to: receive a plurality of glucose levels over a first timeperiod form the analyte sensor; receive a first glycated hemoglobin(HbA1c) level corresponding to an end of the first time period; receivea reticulocyte production index (RPI) value corresponding to a timeduring the first time period; determine a red blood cell eliminationconstant (k_(age)) from the RPI value; and determine at least onephysiological parameter selected from the group consisting of: a redblood cell glycation rate constant (k_(gly)), a red blood cellgeneration rate constant (k_(gen)), and an apparent glycation constant(K), based on (1) the plurality of glucose levels, (2) the first HbA1clevel, and (3) the k_(age). Alternatively, the k_(age) calculated basedon the RPI can be received instead of the RPI.

Embodiments C, D, and E may include additional elements, which mayinclude, but are not limited to: Element 1; Element 20: wherein theinstructions which, when executed by the one or more processors, causesthe one or more processors to further: receive a second HbA1c levelcorresponding to a beginning of the first time period and, in someinstances, wherein the first time period is about 30 days or longer andthe plurality of glucose levels occur on average about 24 or more timesdaily; Element 21: configured to receive at least some of the pluralityof glucose levels from an in vivo analyte sensor having a portionpositioned to be in contact with bodily fluid; Element 22: configured toreceive at least some of the plurality of glucose levels from acontinuous glucose monitoring system; Element 23: configured to receiveat least some of the plurality of glucose levels from a subject based onan in vitro glucose level measurement; Element 24: wherein theinstructions which, when executed by the one or more processors, causesthe one or more processors to further: display the at least onephysiological parameter; Element 25: wherein the instructions which,when executed by the one or more processors, causes the one or moreprocessors to further: determine an error associated with the at leastone physiological parameter; and request at least one new glucose leveland/or request at least one new HbA1c level when the error is at orgreater than about 7%; Element 26: wherein the instructions which, whenexecuted by the one or more processors, causes the one or moreprocessors to further: calculate a metabolic age based on k_(age) and/orK and, in some instances, output a report that includes the metabolicage; Element 27: wherein the instructions which, when executed by theone or more processors, causes the one or more processors to further:calculate a personalized-target glucose range based on the at least onephysiological parameter and, in some instances, output a report thatincludes the personalized-target glucose range; Element 28: wherein theinstructions which, when executed by the one or more processors, causesthe one or more processors to further: calculate a personalized-targetaverage glucose based on the at least one physiological parameter and,in some instances, output a report that includes the personalized-targetaverage glucose; Element 29: wherein the instructions which, whenexecuted by the one or more processors, causes the one or moreprocessors to further: calculate a cHbA1c based on the at least onephysiological parameter and a plurality of glucose levels over a secondtime period following the first time period and, in some instances,output a report that includes the cHbA1c, and, in some instances,wherein the plurality of glucose levels over the second time period areat a plurality of times separated by a time interval t_(i) that is (a)greater than or equal to three hours and less than or equal totwenty-four hours, (b) at least twice a maximum gap duration betweentemporally adjacent glucose levels in the plurality of glucose levelsover the second time period, or (c) both (a) and (b); Element 30:wherein the instructions which, when executed by the one or moreprocessors, causes the one or more processors to further: output atriage recommendation for a subject's treatment based on the at leastone physiological parameter and, in some instances, output a report thatincludes the triage recommendation; Element 31: wherein the instructionswhich, when executed by the one or more processors, causes the one ormore processors to further: output a dosage of diabetes medication basedon the at least one physiological parameter and, in some instances,output a report that includes the dosage of diabetes medication; Element32: wherein the instructions which, when executed by the one or moreprocessors, causes the one or more processors to further: output adosage of glycation medication based on the at least one physiologicalparameter and, in some instances, output a report that includes thedosage of glycation medication; Element 33: wherein the instructionswhich, when executed by the one or more processors, causes the one ormore processors to further: output an abnormal or diseased physiologicalcondition of a subject based on the at least one physiological parameterand, in some instances, output a report that includes the abnormal ordiseased physiological condition; and Element 34: wherein theinstructions which, when executed by the one or more processors, causesthe one or more processors to further: output a type of a medication orsupplement in a subject's body based on the at least one physiologicalparameter and, in some instances, output a report that includes the typeof the medication or supplement. Examples of combinations of elementsinclude, but are not limited to, two or more of Elements 26-34 incombination; two or more of Elements 21-25 in combination; one or moreof Elements 21-25 in combination with one or more of Elements 26-34;Element 1 or Element 20 in combination with one or more of Elements21-34 including any of the foregoing combinations of Elements 21-34.

Embodiment F is a system comprising: an analyte sensor configured tomeasure a glucose level in a bodily fluid; and a monitoring devicecomprising: one or more processors; and a memory operatively coupled tothe one or more processors and having instructions stored thereon which,when executed by the one or more processors, causes the one or moreprocessors to: determine, based on (1) a plurality of first glucoselevels taken over a first time period and (2) a first HbA1c level, atleast one physiological parameter selected from the group consisting of:a red blood cell glycation rate constant (k_(gly)), a red blood cellgeneration rate constant (k_(gen)), a red blood cell eliminationconstant (k_(age)), and an apparent glycation constant (K); determine acalculated glycated hemoglobin (cHbA1c) level based on the at least onephysiological parameter and a plurality of second glucose levels at aplurality of times separated by a time interval t_(i), wherein the timeinterval t_(i) is (a) greater than or equal to three hours and less thanor equal to twenty-four hours, (b) at least twice a maximum gap betweentemporally adjacent glucose levels present in the plurality of secondglucose levels, or (c) both (a) and (b), wherein the plurality of secondglucose levels is for a second time period after the first time period;and output the cHbA1c level.

Embodiment G is a computer implemented method for determining a glycatedhemoglobin level comprising: determining, based on (1) a plurality offirst glucose levels taken over a first time period and (2) a firstHbA1c level corresponding to an end of the first time period, at leastone physiological parameter selected from the group consisting of: a redblood cell glycation rate constant (k_(gly)), a red blood cellgeneration rate constant (k_(gen)), a red blood cell eliminationconstant (k_(age)), and an apparent glycation constant (K); determininga calculated glycated hemoglobin (cHbA1c) level based on the at leastone physiological parameter and a plurality of second glucose levels ata plurality of times separated by a time interval t_(i), wherein thetime interval t_(i) is (a) greater than or equal to three hours and lessthan or equal to twenty-four hours, (b) at least twice a maximum gapbetween temporally adjacent glucose levels present in the plurality ofsecond glucose levels, or (c) both (a) and (b) wherein the plurality ofsecond glucose levels is for a second time period after the first timeperiod; and outputting the cHbA1c level.

Embodiments F or G may include additional elements, which may include,but are not limited to: Element 1, Element 2, Element 3 (as applied toeither or both of the plurality of first glucose levels and theplurality of second glucose levels), Element 4, Element 5, Element 6 (asapplied to either or both of the plurality of first glucose levels andthe plurality of second glucose levels), Element 7 (as applied to eitheror both of the plurality of first glucose levels and the plurality ofsecond glucose levels), one or more of Elements 8-14, and one or more ofElements 16-19. Examples of combinations of elements include, but arenot limited to, two or more of Elements 11-19 in combination; Elements 6and 7 in combination, optionally in further combination with Element 8;Element 6 in combination with Element 3, optionally in furthercombination with one or both of Elements 4-5; Elements 9-10 incombination; Element 9 and/or Element 10 in combination with one or moreof Elements 11-14 and 16-19; one or more of Elements 3-8 in combinationwith one or more of Elements 11-14 and 16-19; and Element 1 or Element 2in combination with one or more of Elements 3-14 and 16-19 including anyof the foregoing combinations of Elements 3-14 and 16-19.

Embodiment H is a system for determining a matched group of participantsin a peer-based treatment program comprising: an analyte sensorconfigured to measure a glucose level in a bodily fluid; and amonitoring device comprising: one or more processors; and a memoryoperatively coupled to the one or more processors and havinginstructions stored thereon which, when executed by the one or moreprocessors, causes the one or more processors to: determine, based on(1) a plurality of first glucose levels taken over a first time periodand (2) a first HbA1c level, at least one physiological parameterselected from the group consisting of: a red blood cell glycation rateconstant (k_(gly)), a red blood cell generation rate constant (k_(gen)),a red blood cell elimination constant (k_(age)), and an apparentglycation constant (K); determine a matched group of participants in apeer-based treatment program using the at least one physiologicalparameter; and output the matched group of participants.

Embodiment I is a computer implemented method for determining a matchedgroup of participants in a peer-based treatment program comprising:determining, based on (1) a plurality of first glucose levels taken overa first time period and (2) a first HbA1c level corresponding to an endof the first time period, at least one physiological parameter selectedfrom the group consisting of: a red blood cell glycation rate constant(k_(gly)), a red blood cell generation rate constant (k_(gen)), a redblood cell elimination constant (k_(age)), and an apparent glycationconstant (K); determining a matched group of participants in apeer-based treatment program using the at least one physiologicalparameter; and outputting the matched group of participants.

Embodiments H or I may include additional elements, which may include,but are not limited to: Element 1, Element 2, Element 3 (as applied toeither or both of the plurality of first glucose levels and theplurality of second glucose levels), Element 4, Element 5, Element 6 (asapplied to either or both of the plurality of first glucose levels andthe plurality of second glucose levels), Element 7 (as applied to eitheror both of the plurality of first glucose levels and the plurality ofsecond glucose levels), and one or more of Elements 8-19. Examples ofcombinations of elements include, but are not limited to, two or more ofElements 11-19 in combination; Elements 6 and 7 in combination,optionally in further combination with Element 8; Element 6 incombination with Element 3, optionally in further combination with oneor both of Elements 4-5; Elements 9-10 in combination; Element 9 and/orElement 10 in combination with one or more of Elements 11-19; one ormore of Elements 3-8 in combination with one or more of Elements 11-19;and Element 1 or Element 2 in combination with one or more of Elements3-19 including any of the foregoing combinations of Elements 3-19.

Unless otherwise indicated, all numbers expressing quantities and thelike in the present specification and associated claims are to beunderstood as being modified in all instances by the term “about.”Accordingly, unless indicated to the contrary, the numerical parametersset forth in the following specification and attached claims areapproximations that may vary depending upon the desired propertiessought to be obtained by the embodiments of the present disclosure. Atthe very least, and not as an attempt to limit the application of thedoctrine of equivalents to the scope of the claim, each numericalparameter should at least be construed in light of the number ofreported significant digits and by applying ordinary roundingtechniques.

One or more illustrative embodiments incorporating various features arepresented herein. Not all features of a physical implementation aredescribed or shown in this application for the sake of clarity. It isunderstood that in the development of a physical embodimentincorporating the embodiments of the present disclosure, numerousimplementation-specific decisions must be made to achieve thedeveloper's goals, such as compliance with system-related,business-related, government-related and other constraints, which varyby implementation and from time to time. While a developer's effortsmight be time-consuming, such efforts would be, nevertheless, a routineundertaking for those of ordinary skill in the art and having benefit ofthis disclosure.

While various systems, tools and methods are described herein in termsof “comprising” various components or steps, the systems, tools andmethods can also “consist essentially of” or “consist of” the variouscomponents and steps.

As used herein, the phrase “at least one of” preceding a series ofitems, with the terms “and” or “or” to separate any of the items,modifies the list as a whole, rather than each member of the list (i.e.,each item). The phrase “at least one of” allows a meaning that includesat least one of any one of the items, and/or at least one of anycombination of the items, and/or at least one of each of the items. Byway of example, the phrases “at least one of A, B, and C” or “at leastone of A, B, or C” each refer to only A, only B, or only C; anycombination of A, B, and C; and/or at least one of each of A, B, and C.

To facilitate a better understanding of the embodiments of the presentinvention, the following examples of preferred or representativeembodiments are given. In no way should the following examples be readto limit, or to define, the scope of the invention.

Examples

Data from 148 type 2 and 139 type 1 subjects enrolled in two previousclinical studies having six months of continuous glucose monitoring wereanalyzed. Only 90 subjects had sufficient data to meet the kinetic modelassumptions described above having data with no continuous glucose datagap 12 hours or longer. Study participants had three HbA1c measurements,on days 1, 100 (±5 days), and 200 (±5 days), as well as frequentsubcutaneous glucose monitoring throughout the analysis time period,which allowed for analysis of two independent data sections (days 1-100and days 101-200) per participant.

The first data section (days 1-100) was used to numerically estimateindividual k_(gly) and k_(age), which allows prospective calculation ofending cHbA1c of the second data section (days 101-200). This endingcHbA1c can be compared with the observed ending HbA1c to validate thekinetic model described herein. For comparison, an estimated HbA1c forthe second data section was calculated based on (1) 14-day mean and (2)14-day weighted average glucose converted by the accepted regressionmodel from the A1c-Derived Average Glucose (ADAG) study, which bothassume k_(gly) is a constant, which as discussed previously is thecurrently accepted method of relating HbA1c to glucose measurements.

FIGS. 9A-C illustrate a comparison between the laboratory HbA1c levelsat day 200 (±5 days) relative to the estimated HbA1c values, where theeHbA1c values in the 9A plot are calculated using the 14-day mean model,the eHbA1c values in the 9B plot are calculated using the 14-dayweighted average model, and the cHbA1c values in the 9C plot arecalculated using the kinetic model described herein (Equation 8). Thesolid line in all graphs illustrates the linear regression of thecomparative HbA1c values for the corresponding models. The dashed lineis a one-to-one line, where the closer the solid line linear regressionis thereto, the better the model. Clearly, the kinetic model describedherein models the data better, which illustrates that k_(age) andk_(gly) are individualized, which is a novel way to approach correlatingHbA1c to glucose measurements.

FIG. 10 illustrates an example study subject's data with the measuredglucose levels (solid line), laboratory HbA1c readings (open circles),cHbA1c model values (long dashed line), and 14-day eHbA1c model values(dotted line). The cHbA1c model values in FIG. 10 were calculated usingthe physiological parameters (k_(age) and k_(gly)). The physiologicalparameters were calculated based on the first two laboratory HbA1creadings and glucose levels measured between the first two laboratoryHbA1c readings. The 14-day eHbA1c values are glucose level 14-dayrunning averages during the study.

The FIG. 10 example shows the dynamic nature of the glucose-to-cHbA1cand glucose-to-eHbA1c relationships. Additional examples were determinedfor type 1 and type 2 diabetes study participants across a range ofprediction deviations: 25th, 50th and 75th percentiles for the cHbA1cmethod. In these examples, the disagreement between the cHbA1c from the14-day average glucose indicates the exaggerated amplitude of variationinherent in the simple 14-day method.

FIG. 11 illustrates the relationship between steady glucose andequilibrium HbA1c (1) as determined using the standard conversion ofHbA1c to estimated average glucose (dashed line with error bars) and (2)as measured for the 90 participants (solid lines). These individualcurves (solid lines) represent the agreement of average glucose withlaboratory measure HbA1c under the condition of their average glucoselevel being stable for days-to-weeks. The model suggests that therelationship of glucose-to-HbA1c is not constant, with larger changes inglucose needed to achieve the same change in HbA1c as levels of thelatter marker increase. Contrary to prior assessments of the glycationindex, the kinetic model of the present disclosure suggests that anindividual's glycation index will not be constant across all levels ofHbA1c. Unlike eHbA1c, a key advantage of cHbA1c is its ability toaccount for individual variation in glycation. Individuals with lower Kare “low glycators”, and have higher average glucose levels for a givenHbA1c level, with the reverse being true for those with high K values.

Using the kinetic model of the present disclosure, a relationshipbetween K (dL/mg) and mean glucose level target (mg/dL) is illustratedin FIG. 12 plotted for varying HbA1c target values. That is, if asubject is targeting a specific HbA1c value (e.g., for a subsequentHbA1c measurement or cHbA1c estimation) and has a known K value (e.g.,based on a plurality of measured glucose levels and at least onemeasured HbA1c), a mean glucose target can be derived and/or identifiedfor the subject over the time period in which the subject is targetingthe HbA1c value.

Therefore, the disclosed systems, tools and methods are well adapted toattain the ends and advantages mentioned as well as those that areinherent therein. The particular embodiments disclosed above areillustrative only, as the teachings of the present disclosure may bemodified and practiced in different but equivalent manners apparent tothose skilled in the art having the benefit of the teachings herein.Furthermore, no limitations are intended to the details of constructionor design herein shown, other than as described in the claims below. Itis therefore evident that the particular illustrative embodimentsdisclosed above may be altered, combined, or modified and all suchvariations are considered within the scope of the present disclosure.The systems, tools and methods illustratively disclosed herein maysuitably be practiced in the absence of any element that is notspecifically disclosed herein and/or any optional element disclosedherein. While systems, tools and methods are described in terms of“comprising,” “containing,” or “including” various components or steps,the systems, tools and methods can also “consist essentially of” or“consist of” the various components and steps. All numbers and rangesdisclosed above may vary by some amount. Whenever a numerical range witha lower limit and an upper limit is disclosed, any number and anyincluded range falling within the range is specifically disclosed. Inparticular, every range of values (of the form, “from about a to aboutb,” or, equivalently, “from approximately a to b,” or, equivalently,“from approximately a-b”) disclosed herein is to be understood to setforth every number and range encompassed within the broader range ofvalues. Also, the terms in the claims have their plain, ordinary meaningunless otherwise explicitly and clearly defined by the patentee.Moreover, the indefinite articles “a” or “an,” as used in the claims,are defined herein to mean one or more than one of the elements that itintroduces. If there is any conflict in the usages of a word or term inthis specification and one or more patent or other documents that may beincorporated herein by reference, the definitions that are consistentwith this specification should be adopted.

1. A method comprising: measuring a plurality of first glucose levelsover a first time period; measuring a first glycated hemoglobin (HbA1c)level corresponding to an end of the first time period; measuring areticulocyte production index (RPI) value; calculating a red blood cellelimination constant (k_(age)) based on the RPI value; calculating atleast one physiological parameter selected from the group consisting of:a red blood cell glycation rate constant (k_(gly)), a red blood cellgeneration rate constant (k_(gen)), and an apparent glycation constant(K), based on (1) the plurality of first glucose levels, (2) the firstHbA1c level, and (3) the k_(age); and adjusting a glucose level targetbased on the at least one physiological parameter.
 2. The method ofclaim 1, wherein determining k_(age) is based on RPI=k_(age)/(3.47day⁻¹*(1−ln 2)).
 3. The method of claim 1, wherein the glucose leveltarget is one or more value selected from the group consisting of apersonalized lower glucose limit, a personalized upper glucose limit,and a personalized-target glucose average.
 4. The method of claim 3,wherein the at least one physiological parameter comprises K, and thepersonalized upper glucose limit equals 0.087/K.
 5. The method of claim3, wherein the at least one physiological parameter comprises k_(gly),and wherein the personalized lower glucose limit equals3.35×10⁻⁴/k_(gly).
 6. The method of claim 3, wherein the at least onephysiological parameter comprises K, and wherein the personalized-targetaverage glucose (GT) equals AT/(K(1−AT)) where AT is a target HbA1clevel.
 7. The method of claim 1 further comprising: measuring a secondHbA1c corresponding to a beginning of the first time period, whereindetermination of the at least one physiological parameter is furtherbased on (4) the second HbA1c.
 8. The method of claim 1 furthercomprising: calculating an error associated with the at least onephysiological parameter; and receiving at least one new glucose leveland/or receiving at least one new HbA1c level when the error is at orgreater than about 7%.
 9. The method of claim 1 further comprising:treating a subject based on the glucose level target.
 10. The method ofclaim 9, wherein treating the subject comprises administering and/oradjusting: an insulin dosage, a glycation-medication dosage, an exerciseregime, a meal intake, or a combination thereof.
 11. The method of claim1, wherein the plurality of first glucose levels are measured in abodily fluid selected from the group consisting of: blood, dermal fluid,interstitial fluid, or a combination thereof.
 12. The method of claim 1further comprising: displaying the glucose level target.
 13. The methodof claim 1 further comprising: receiving a glucose level of a subjectafter adjusting the glucose level target; and displaying an alarm whenthe glucose level is outside the glucose level target.
 14. The method ofclaim 1 further comprising: calculating a metabolic age based on k_(age)and/or K.
 15. The method of claim 1 further comprising: receiving aplurality of second glucose levels for a second time period after thefirst time period; and determining a calculated glycated hemoglobin(cHbA1c) level based on (1) the k_(gly), (2) the k_(age), (3) theplurality of second glucose levels for the second time period, and (4)the first HbA1c level.
 16. The method of claim 1 further comprising:receiving a second HbA1c level for an end of a second time periodfollowing the first time period; calculating at least one secondphysiological parameter corresponding to the at least one firstphysiological parameter; and identifying (1) a presence of an abnormalor diseased physiological condition and/or (2) an indicator of dopingbased on a comparison of the at least one first physiological parameterand the at least one second physiological parameter.
 17. A systemcomprising: an analyte sensor configured to measure a glucose level in abodily fluid; and a monitoring device comprising: one or moreprocessors; and a memory operatively coupled to the one or moreprocessors and having instructions stored thereon which, when executedby the one or more processors, causes the one or more processors to:receive a plurality of first glucose levels in the bodily fluid over afirst time period from the analyte sensor; receive a first glycatedhemoglobin (HbA1c) level corresponding to an end of the first timeperiod; receive a reticulocyte production index (RPI) value; determine ared blood cell elimination constant (k_(age)) based on the RPI value;determine at least one physiological parameter selected from the groupconsisting of: a red blood cell glycation rate constant (k_(gly)), a redblood cell generation rate constant (k_(gen)), and an apparent glycationconstant (K), based on (1) the plurality of first glucose levels, (2)the first HbA1c level, and k_(age); and adjust a glucose level targetbased on the at least one physiological parameter. 18.-22. (canceled)23. The system of claim 17 further comprising: a display, wherein theinstructions which, when executed by the one or more processors, causesthe one or more processors to further: display the glucose level target.24.-26. (canceled)
 27. The system of claim 17, wherein the instructionswhich, when executed by the one or more processors, causes the one ormore processors to further: receive a plurality of second glucose levelsfor a second time period after the first time period from the analytesensor; and determine a calculated glycated hemoglobin (cHbA1c) levelbased on (1) the k_(gly), (2) the k_(age), (3) the plurality of secondglucose levels for the second time period, and (4) the first HbA1clevel.
 28. (canceled)
 29. The system of claim 17, wherein theinstructions, when executed, cause the one or more processors to:determine an insulin dosage based on the glucose level target; andtransmit the insulin dosage to an insulin pump system. 30.-33.(canceled)